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Williams, Ross Neil. "Adaptive data compression." Adelaide, 1989. http://web4.library.adelaide.edu.au/theses/09PH/09phw7262.pdf.
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Steinruecken, Christian. "Lossless data compression." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709134.
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Lindsay, Robert A., and B. V. Cox. "UNIVERSAL DATA COMPRESSION." International Foundation for Telemetering, 1985. http://hdl.handle.net/10150/615552.
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International Telemetering Conference Proceedings / October 28-31, 1985 / Riviera Hotel, Las Vegas, NevadaUniversal and adaptive data compression techniques have the capability to globally compress all types of data without loss of information but have the disadvantage of complexity and computation speed. Advances in hardware speed and the reduction of computational costs have made universal data compression feasible. Implementations of the Adaptive Huffman and Lempel-Ziv compression algorithms are evaluated for performance. Compression ratios versus run times for different size data files are graphically presented and discussed in the paper. Required adjustments needed for optimum performance of the algorithms relative to theoretical achievable limits will be outlined.
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Radhakrishnan, Radhika. "Genome data modeling and data compression." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447611.
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García, Sobrino Francisco Joaquín. "Sounder spectral data compression." Doctoral thesis, Universitat Autònoma de Barcelona, 2018. http://hdl.handle.net/10803/663984.
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IASI (Infrared Atmospheric Sounding Interferometer) es un espectrómetro basado en la transformada de Fourier diseñado para medir radiación infrarroja emitida por La Tierra. A partir de estas mediciones se generan datos con una precisión y resolución espectral sin precedentes. Esta información es útil para obtener perfiles de temperatura y humedad, así como concentraciones de gases traza, que son esenciales para la comprensión y monitorización del clima y para realizar previsiones atmosféricas.
La alta resolución espectral, espacial y temporal de los datos producidos por el instrumento implica generar productos con un tamaño considerablemente grande, lo que demanda el uso de técnicas de compresión eficientes para mejorar tanto las capacidades de transmisión como las de almacenamiento. En esta tesis se realiza un exhaustivo análisis de la compresión de datos IASI y se proporcionan recomendaciones para generar datos reconstruidos útiles para el usuario final. En este análisis se utilizan datos IASI transmitidos a las estaciones de recepción (productos IASI L0) y datos destinados a usuarios finales que son distribuidos a centros de predicción numérica y a la comunidad científica en general (productos IASI L1C).
Para comprender mejor la naturaleza de los datos capturados por el instrumento se analizan las estadísiticas de la información y el rendimiento de varias técnicas de compresión en datos IASI L0. Se estudia la entropía de orden-0 y las entropías contextuales de orden-1, orden-2 y orden-3. Este estudio revela que el tamaño de los datos se podría reducir considerablemente explotando la entropía de orden-0. Ganancias más significativas se podrían conseguir si se utilizaran modelos contextuales. También se investiga el rendimiento de varias técnicas de compresión sin pérdida. Los resultados experimentales sugieren que se puede alcanzar un ratio de compresión de 2,6:1, lo que implica que sería posible transmitir más datos manteniendo la tasa de transmisión original o, como alternativa, la tasa de transmisión del instrumento se podría reducir.
También se realiza un exhaustivo análisis de la compresión de datos IASI L1C donde se evalúa el rendimiento de varias transformadas espectrales y técnicas de compresión. La experimentación abarca compresión sin pérdida, compresión casi sin pérdida y compresión con pérdida sobre una amplia gama de productos IASI-A e IASI-B. Para compresión sin pérdida es posible conseguir ratios de compresión superiores a 2,5:1. Para compresión casi sin pérdida y compresión con pérdida se pueden alcanzar ratios de compresión mayores a la vez que se producen espectros reconstruidos de calidad.
Aunque la compresión casi sin pérdida y la compresión con pérdida producen ratios de compresión altos, la utilidad de los espectros reconstruidos se puede ver comprometida ya que cierta información es eliminada durante la etapa de compresión. En consecuencia, se estudia el impacto de la compresión casi sin pérdida y la compresión con pérdida en aplicaciones de usuario final. Concretamente, se evalúa el impacto de la compresión en datos IASI L1C cuando algoritmos estadísticos se utilizan posteriormente para predecir información física a partir de los espectros reconstruidos. Los resultados experimentales muestran que el uso de espectros reconstruidos puede conseguir resultados de predicción competitivos, mejorando incluso los resultados que se obtienen cuando se utilizan datos sin comprimir.
A partir del análisis previo se estudia el origen de los beneficios que produce la compresión obteniendo dos observaciones principales. Por un lado, la compresión produce eliminación de ruido y filtrado de la señal lo que beneficia a los métodos de predicción. Por otro lado, la compresión es una forma indirecta de producir regularización espectral y espacial entre píxeles vecinos lo que beneficia a algoritmos que trabajan a nivel de píxel.The Infrared Atmospheric Sounding Interferometer (IASI) is a Fourier Transform Spectrometer implemented on the MetOp satellite series. The instrument is intended to measure infrared radiation emitted from the Earth. IASI produces data with unprecedented accuracy and spectral resolution. Notably, the sounder harvests spectral information to derive temperature and moisture profiles, as well as concentrations of trace gases, essential for the understanding of weather, for climate monitoring, and for atmospheric forecasts. The large spectral, spatial, and temporal resolution of the data collected by the instrument involves generating products with a considerably large size, about 16 Gigabytes per day by each of the IASI-A and IASI-B instruments currently operated. The amount of data produced by IASI demands efficient compression techniques to improve both the transmission and the storage capabilities. This thesis supplies a comprehensive analysis of IASI data compression and provides effective recommendations to produce useful reconstructed spectra. The study analyzes data at different processing stages. Specifically, we use data transmitted by the instrument to the reception stations (IASI L0 products) and end-user data disseminated to the Numerical Weather Prediction (NWP) centres and the scientific community (IASI L1C products). In order to better understand the nature of the data collected by the instrument, we analyze the information statistics and the compression performance of several coding strategies and techniques on IASI L0 data. The order-0 entropy and the order-1, order-2, and order-3 context-based entropies are analyzed in several IASI L0 products. This study reveals that the size of the data could be considerably reduced by exploiting the order-0 entropy. More significant gains could be achieved if contextual models were used. We also investigate the performance of several state-of-the-art lossless compression techniques. Experimental results suggest that a compression ratio of 2.6:1 can be achieved, which involves that more data could be transmitted at the original transmission rate or, alternatively, the transmission rate of the instrument could be further decreased. A comprehensive study of IASI L1C data compression is performed as well. Several state-of-the-art spectral transforms and compression techniques are evaluated on IASI L1C spectra. Extensive experiments, which embrace lossless, near-lossless, and lossy compression, are carried out over a wide range of IASI-A and IASI-B orbits. For lossless compression, compression ratios over 2.5:1 can be achieved. For near-lossless and lossy compression, higher compression ratios can be achieved, while producing useful reconstructed spectra. Even though near-lossless and lossy compression produce higher compression ratios compared to lossless compression, the usefulness of the reconstructed spectra may be compromised because some information is removed during the compression stage. Therefore, we investigate the impact of near-lossless and lossy compression on end-user applications. Specifically, the impact of compression on IASI L1C data is evaluated when statistical retrieval algorithms are later used to retrieve physical information. Experimental results reveal that the reconstructed spectra can enable competitive retrieval performance, improving the results achieved for the uncompressed data, even at high compression ratios. We extend the previous study to a real scenario, where spectra from different disjoint orbits are used in the retrieval stage. Experimental results suggest that the benefits produced by compression are still significant. We also investigate the origin of these benefits. On the one hand, results illustrate that compression performs signal filtering and denoising, which benefits the retrieval methods. On the other hand, compression is an indirect way to produce spectral and spatial regularization, which helps pixel-wise statistical algorithms.
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Du, Toit Benjamin David. "Data Compression and Quantization." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/79233.
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Data Compression
Due to limitations in data storage and bandwidth, data of all types has often required compression. This need has spawned many different methods of compressing data. In certain situations the fidelity of the data can be compromised and unnecessary information can be discarded, while in other situations, the fidelity of the data is necessary for the data to be useful thereby requiring methods of reducing the data storage requirements without discarding any information.
The theory of data compression has received much attention over the past half century, with some of the most important work done by Claude E. Shannon in the 1940’s and 1950’s and at present topics such as Information and Coding Theory, which encompass a wide variety of sciences, continue to make headway into the interesting and highly applicable topic of data compression.
Quantization
Quantization is a broad notion used in several fields especially in the sciences, including signal processing, quantum physics, computer science, geometry, music and others. The concept of quantization is related to the idea of grouping, dividing or approximating some physical quantity by a set of small discrete measurements.
Data Quantization involves the discretization of data, or the approximation of large data sets by smaller data sets.
This mini dissertation is a research dissertation that considers how data, which is of a statistical nature, can be quantized and compressed.Dissertation (MSc)--University of Pretoria, 2014.
Statistics
MSc
Unrestricted
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Roguski, Łukasz 1987. "High-throughput sequencing data compression." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/565775.
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Thanks to advances in sequencing technologies, biomedical research has experienced a revolution over recent years, resulting in an explosion in the amount of genomic data being generated worldwide. The typical space requirement for storing sequencing data produced by a medium-scale experiment lies in the range of tens to hundreds of gigabytes, with multiple files in different formats being produced by each experiment. The current de facto standard file formats used to represent genomic data are text-based. For practical reasons, these are stored in compressed form. In most cases, such storage methods rely on general-purpose text compressors, such as gzip. Unfortunately, however, these methods are unable to exploit the information models specific to sequencing data, and as a result they usually provide limited functionality and insufficient savings in storage space. This explains why relatively basic operations such as processing, storage, and transfer of genomic data have become a typical bottleneck of current analysis setups.
Therefore, this thesis focuses on methods to efficiently store and compress the data generated from sequencing experiments. First, we propose a novel general purpose FASTQ files compressor. Compared to gzip, it achieves a significant reduction in the size of the resulting archive, while also offering high data processing speed. Next, we present compression methods that exploit the high sequence redundancy present in sequencing data. These methods achieve the best compression ratio among current state-of-the-art FASTQ compressors, without using any external reference sequence. We also demonstrate different lossy compression approaches to store auxiliary sequencing data, which allow for further reductions in size. Finally, we propose a flexible framework and data format, which allows one to semi-automatically generate compression solutions which are not tied to any specific genomic file format. To facilitate data management needed by complex pipelines, multiple genomic datasets having heterogeneous formats can be stored together in configurable containers, with an option to perform custom queries over the stored data. Moreover, we show that simple solutions based on our framework can achieve results comparable to those of state-of-the-art format-specific compressors.
Overall, the solutions developed and described in this thesis can easily be incorporated into current pipelines for the analysis of genomic data. Taken together, they provide grounds for the development of integrated approaches towards efficient storage and management of such data.Gràcies als avenços en el camp de les tecnologies de seqüenciació, en els darrers anys la recerca biomèdica ha viscut una revolució, que ha tingut com un dels resultats l'explosió del volum de dades genòmiques generades arreu del món. La mida típica de les dades de seqüenciació generades en experiments d'escala mitjana acostuma a situar-se en un rang entre deu i cent gigabytes, que s'emmagatzemen en diversos arxius en diferents formats produïts en cada experiment. Els formats estàndards actuals de facto de representació de dades genòmiques són en format textual. Per raons pràctiques, les dades necessiten ser emmagatzemades en format comprimit. En la majoria dels casos, aquests mètodes de compressió es basen en compressors de text de caràcter general, com ara gzip. Amb tot, no permeten explotar els models d'informació especifícs de dades de seqüenciació. És per això que proporcionen funcionalitats limitades i estalvi insuficient d'espai d'emmagatzematge. Això explica per què operacions relativament bàsiques, com ara el processament, l'emmagatzematge i la transferència de dades genòmiques, s'han convertit en un dels principals obstacles de processos actuals d'anàlisi. Per tot això, aquesta tesi se centra en mètodes d'emmagatzematge i compressió eficients de dades generades en experiments de sequenciació. En primer lloc, proposem un compressor innovador d'arxius FASTQ de propòsit general. A diferència de gzip, aquest compressor permet reduir de manera significativa la mida de l'arxiu resultant del procés de compressió. A més a més, aquesta eina permet processar les dades a una velocitat alta. A continuació, presentem mètodes de compressió que fan ús de l'alta redundància de seqüències present en les dades de seqüenciació. Aquests mètodes obtenen la millor ratio de compressió d'entre els compressors FASTQ del marc teòric actual, sense fer ús de cap referència externa. També mostrem aproximacions de compressió amb pèrdua per emmagatzemar dades de seqüenciació auxiliars, que permeten reduir encara més la mida de les dades. En últim lloc, aportem un sistema flexible de compressió i un format de dades. Aquest sistema fa possible generar de manera semi-automàtica solucions de compressió que no estan lligades a cap mena de format específic d'arxius de dades genòmiques. Per tal de facilitar la gestió complexa de dades, diversos conjunts de dades amb formats heterogenis poden ser emmagatzemats en contenidors configurables amb l'opció de dur a terme consultes personalitzades sobre les dades emmagatzemades. A més a més, exposem que les solucions simples basades en el nostre sistema poden obtenir resultats comparables als compressors de format específic de l'estat de l'art. En resum, les solucions desenvolupades i descrites en aquesta tesi poden ser incorporades amb facilitat en processos d'anàlisi de dades genòmiques. Si prenem aquestes solucions conjuntament, aporten una base sòlida per al desenvolupament d'aproximacions completes encaminades a l'emmagatzematge i gestió eficient de dades genòmiques.
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Frimpong-Ansah, K. "Adaptive data compression with memory." Thesis, Imperial College London, 1986. http://hdl.handle.net/10044/1/38008.
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Kretzmann, Jane Lee. "Compression of bitmapped graphic data." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/25761.
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This paper explores the general topic of data compression, with emphasis on application of the techniques to graphic bitmapped data. Run-length encoding, statistical encoding (including Huffman codes), and relative encoding are examined and evaluated. A compression application of the Huffman coding of a run-length encoded file is designed and partially implemented in Chapter VII. A listing of the computer program which performs the compression is included as an appendix. Possibilities for further study are suggested. Data compression, Compression, Graphics, Bitmapped, Computer network, Run-length encoding, Huffman codes, Lossless, Lossy, Relative encoding, Statistical encoding, Computer program. (jes)
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Barr, Kenneth C. (Kenneth Charles) 1978. "Energy aware lossless data compression." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87316.
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Walker, Wendy Tolle 1959. "Video data compression for telescience." Thesis, The University of Arizona, 1988. http://hdl.handle.net/10150/276830.
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This paper recommends techniques to use for data compression of video data used to point a telescope and from a camera observing a robot, for transmission from the proposed U.S. Space Station to Earth. The mathematical basis of data compression is presented, followed by a general review of data compression techniques. A technique that has wide-spread use in data compression of videoconferencing images is recommended for the robot observation data. Bit rates of 60 to 400 kbits/sec can be achieved. Several techniques are modelled to find a best technique for the telescope data. Actual starfield images are used for the evaluation. The best technique is chosen on the basis of which model provides the most compression while preserving the important information in the images. Compression from 8 bits per pel to 0.015 bits per pel is achieved.
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Horan, Sheila. "DATA COMPRESSION STATISTICS AND IMPLICATIONS." International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/608754.
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International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, NevadaBandwidth is a precious commodity. In order to make the best use of what is available, better modulation schemes need to be developed, or less data needs to be sent. This paper will investigate the option of sending less data via data compression. The structure and the entropy of the data determine how much lossless compression can be obtained for a given set of data. This paper shows the data structure and entropy for several actual telemetry data sets and the resulting lossless compression obtainable using data compression techniques.
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Açikel, Ömer Fatih, and William E. Ryan. "Lossless Compression of Telemetry Data." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611434.
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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, CaliforniaSandia National Laboratories is faced with the problem of losslessly compressing digitized data produced by various measurement transducers. Their interest is in compressing the data as it is created, i.e., in real time. In this work we examine a number of lossless compression schemes with an eye toward their compression efficiencies and compression speeds. The various algorithms are applied to data files supplied by Sandia containing actual vibration data.
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Karki, Maya, H. N. Shivashankar, and R. K. Rajangam. "IMAGE DATA COMPRESSION (USING DPCM)." International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612163.
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International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, NevadaAdvances in computer technology and mass storage have paved the way for implementing advanced data compression techniques to improve the efficiency of transmission and storage of images. The present paper deals on the development of a data compression algorithm suitable for images received from satellites. The compression ratio of 1.91:1 is achieved with the proposed technique. The technique used is 1-D DPCM Coding. Hardware-relevant to coder has also been proposed.
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Thornton, Christopher James. "Concept learning as data compression." Thesis, University of Sussex, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.278809.
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Pötzelberger, Klaus, and Helmut Strasser. "Data Compression by Unsupervised Classification." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1997. http://epub.wu.ac.at/974/1/document.pdf.
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This paper deals with a general class of classification methods which are related both to vector quantization in the sense of Pollard, [12], as well as to competitive learning in the sense of Kohonen, [10]. The basic duality of minimum variance partitioning and vector quantization known from statistical cluster analysis is shown to be true for this whole class of classification problems. The paper contains theoretical results like existence of optima, consistency of approximate optima and characterization of local optima as fixpoints of a fix point algorithm. A fix point algorithm is proposed and its termination after finite time is proved for empirical distributions. The construction of a particular classification method is based on a statistical information measure specified by a convex function. Modifying this convex function gives room for suggesting a large variety of new classification procedures, e.g. of robust quantifiers. (author's abstract)Series: Forschungsberichte / Institut für Statistik
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Deng, Mo Ph D. Massachusetts Institute of Technology. "On compression of encrypted data." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106100.
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Thesis: S.M. in Electrical Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 93-96).
In this thesis, I took advantage of a model-free compression architecture, where the encoder only makes decision about coding and leaves to the decoder to apply the knowledge of the source for decoding, to attack the problem of compressing encrypted data. Results for compressing different sources encrypted by different class of ciphers are shown and analyzed. Moreover, we generalize the problem from encryption schemes to operations, or data-processing techniques. We try to discover key properties an operation should have, in order to enable good post-operation compression performances.
by Mo Deng.
S.M. in Electrical Engineering
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Al-Rababa'a, Ahmad. "Arithmetic bit recycling data compression." Doctoral thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/26759.
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Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2015-2016La compression des données est la technique informatique qui vise à réduire la taille de l'information pour minimiser l'espace de stockage nécessaire et accélérer la transmission des données dans les réseaux à bande passante limitée. Plusieurs techniques de compression telles que LZ77 et ses variantes souffrent d'un problème que nous appelons la redondance causée par la multiplicité d'encodages. La multiplicité d'encodages (ME) signifie que les données sources peuvent être encodées de différentes manières. Dans son cas le plus simple, ME se produit lorsqu'une technique de compression a la possibilité, au cours du processus d'encodage, de coder un symbole de différentes manières. La technique de compression par recyclage de bits a été introduite par D. Dubé et V. Beaudoin pour minimiser la redondance causée par ME. Des variantes de recyclage de bits ont été appliquées à LZ77 et les résultats expérimentaux obtenus conduisent à une meilleure compression (une réduction d'environ 9% de la taille des fichiers qui ont été compressés par Gzip en exploitant ME). Dubé et Beaudoin ont souligné que leur technique pourrait ne pas minimiser parfaitement la redondance causée par ME, car elle est construite sur la base du codage de Huffman qui n'a pas la capacité de traiter des mots de code (codewords) de longueurs fractionnaires, c'est-à-dire qu'elle permet de générer des mots de code de longueurs intégrales. En outre, le recyclage de bits s'appuie sur le codage de Huffman (HuBR) qui impose des contraintes supplémentaires pour éviter certaines situations qui diminuent sa performance. Contrairement aux codes de Huffman, le codage arithmétique (AC) peut manipuler des mots de code de longueurs fractionnaires. De plus, durant ces dernières décennies, les codes arithmétiques ont attiré plusieurs chercheurs vu qu'ils sont plus puissants et plus souples que les codes de Huffman. Par conséquent, ce travail vise à adapter le recyclage des bits pour les codes arithmétiques afin d'améliorer l'efficacité du codage et sa flexibilité. Nous avons abordé ce problème à travers nos quatre contributions (publiées). Ces contributions sont présentées dans cette thèse et peuvent être résumées comme suit. Premièrement, nous proposons une nouvelle technique utilisée pour adapter le recyclage de bits qui s'appuie sur les codes de Huffman (HuBR) au codage arithmétique. Cette technique est nommée recyclage de bits basé sur les codes arithmétiques (ACBR). Elle décrit le cadriciel et les principes de l'adaptation du HuBR à l'ACBR. Nous présentons aussi l'analyse théorique nécessaire pour estimer la redondance qui peut être réduite à l'aide de HuBR et ACBR pour les applications qui souffrent de ME. Cette analyse démontre que ACBR réalise un recyclage parfait dans tous les cas, tandis que HuBR ne réalise de telles performances que dans des cas très spécifiques. Deuxièmement, le problème de la technique ACBR précitée, c'est qu'elle requiert des calculs à précision arbitraire. Cela nécessite des ressources illimitées (ou infinies). Afin de bénéficier de cette dernière, nous proposons une nouvelle version à précision finie. Ladite technique devienne ainsi efficace et applicable sur les ordinateurs avec les registres classiques de taille fixe et peut être facilement interfacée avec les applications qui souffrent de ME. Troisièmement, nous proposons l'utilisation de HuBR et ACBR comme un moyen pour réduire la redondance afin d'obtenir un code binaire variable à fixe. Nous avons prouvé théoriquement et expérimentalement que les deux techniques permettent d'obtenir une amélioration significative (moins de redondance). À cet égard, ACBR surpasse HuBR et fournit une classe plus étendue des sources binaires qui pouvant bénéficier d'un dictionnaire pluriellement analysable. En outre, nous montrons qu'ACBR est plus souple que HuBR dans la pratique. Quatrièmement, nous utilisons HuBR pour réduire la redondance des codes équilibrés générés par l'algorithme de Knuth. Afin de comparer les performances de HuBR et ACBR, les résultats théoriques correspondants de HuBR et d'ACBR sont présentés. Les résultats montrent que les deux techniques réalisent presque la même réduction de redondance sur les codes équilibrés générés par l'algorithme de Knuth.
Data compression aims to reduce the size of data so that it requires less storage space and less communication channels bandwidth. Many compression techniques (such as LZ77 and its variants) suffer from a problem that we call the redundancy caused by the multiplicity of encodings. The Multiplicity of Encodings (ME) means that the source data may be encoded in more than one way. In its simplest case, it occurs when a compression technique with ME has the opportunity at certain steps, during the encoding process, to encode the same symbol in different ways. The Bit Recycling compression technique has been introduced by D. Dubé and V. Beaudoin to minimize the redundancy caused by ME. Variants of bit recycling have been applied on LZ77 and the experimental results showed that bit recycling achieved better compression (a reduction of about 9% in the size of files that have been compressed by Gzip) by exploiting ME. Dubé and Beaudoin have pointed out that their technique could not minimize the redundancy caused by ME perfectly since it is built on Huffman coding, which does not have the ability to deal with codewords of fractional lengths; i.e. it is constrained to generating codewords of integral lengths. Moreover, Huffman-based Bit Recycling (HuBR) has imposed an additional burden to avoid some situations that affect its performance negatively. Unlike Huffman coding, Arithmetic Coding (AC) can manipulate codewords of fractional lengths. Furthermore, it has attracted researchers in the last few decades since it is more powerful and flexible than Huffman coding. Accordingly, this work aims to address the problem of adapting bit recycling to arithmetic coding in order to improve the code effciency and the flexibility of HuBR. We addressed this problem through our four (published) contributions. These contributions are presented in this thesis and can be summarized as follows. Firstly, we propose a new scheme for adapting HuBR to AC. The proposed scheme, named Arithmetic-Coding-based Bit Recycling (ACBR), describes the framework and the principle of adapting HuBR to AC. We also present the necessary theoretical analysis that is required to estimate the average amount of redundancy that can be removed by HuBR and ACBR in the applications that suffer from ME, which shows that ACBR achieves perfect recycling in all cases whereas HuBR achieves perfect recycling only in very specific cases. Secondly, the problem of the aforementioned ACBR scheme is that it uses arbitrary-precision calculations, which requires unbounded (or infinite) resources. Hence, in order to benefit from ACBR in practice, we propose a new finite-precision version of the ACBR scheme, which makes it efficiently applicable on computers with conventional fixed-sized registers and can be easily interfaced with the applications that suffer from ME. Thirdly, we propose the use of both techniques (HuBR and ACBR) as the means to reduce the redundancy in plurally parsable dictionaries that are used to obtain a binary variable-to-fixed length code. We theoretically and experimentally show that both techniques achieve a significant improvement (less redundancy) in this respect, but ACBR outperforms HuBR and provides a wider class of binary sources that may benefit from a plurally parsable dictionary. Moreover, we show that ACBR is more flexible than HuBR in practice. Fourthly, we use HuBR to reduce the redundancy of the balanced codes generated by Knuth's algorithm. In order to compare the performance of HuBR and ACBR, the corresponding theoretical results and analysis of HuBR and ACBR are presented. The results show that both techniques achieved almost the same significant reduction in the redundancy of the balanced codes generated by Knuth's algorithm.
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Dušák, Petr. "Fractal application in data compression." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-201795.
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The mission of the Technology Transfer Programme Office is to increase impact on a society by transferring technologies developed by the European Space Agency. Method and Apparatus for compressing time series is a patented compression algorithm designed to be efficient as its purpose is to run on deep space probes or satellites. The algorithm is inspired by a method for fractal terrain generation, namely the midpoint displacement algorithm. This work introduces fractals, their application and modifying the patented algorithm, in order to achieve greater compression. The modification lies in modifying the displacement mechanism. The modified algorithm is capable of reducing data up to 25 %, compared to the patented algorithm. The modification made the algorithm less efficient. In large-scale test, performed on Rosetta spacecraft telemetry, the modified algorithm achieved around 5 % higher compression.
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Singh, Inderpreet. "New wavelet transforms and their applications to data compression." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0011/NQ52773.pdf.
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Senecal, Joshua G. "Length-limited data transformation and compression /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2005. http://uclibs.org/PID/11984.
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Arbring, Joel, and Patrik Hedström. "On Data Compression for TDOA Localization." Thesis, Linköping University, Information Coding, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57274.
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This master thesis investigates different approaches to data compression on common types of signals in the context of localization by estimating time difference of arrival (TDOA). The thesis includes evaluation of the compression schemes using recorded data, collected as part of the thesis work. This evaluation shows that compression is possible while preserving localization accuracy.
The recorded data is backed up with more extensive simulations using a free space propagation model without attenuation. The signals investigated are flat spectrum signals, signals using phase-shift keying and single side band speech signals. Signals with low bandwidth are given precedence over high bandwidth signals, since they require more data in order to get an accurate localization estimate.
The compression methods used are transform based schemes. The transforms utilized are the Karhunen-Loéve transform and the discrete Fourier transform. Different approaches for quantization of the transform components are examined, one of them being zonal sampling.
Localization is performed in the Fourier domain by calculating the steered response power from the cross-spectral density matrix. The simulations are performed in Matlab using three recording nodes in a symmetrical geometry.
The performance of localization accuracy is compared with the Cramér-Rao bound for flat spectrum signals using the standard deviation of the localization error from the compressed signals.
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Røsten, Tage. "Seismic data compression using subband coding." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2000. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1487.
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Watkins, Bruce E. "Data compression using artificial neural networks." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/25801.
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Approved for public release; distribution is unlimitedThis thesis investigates the application of artificial neural networks for the compression of image data. An algorithm is developed using the competitive learning paradigm which takes advantage of the parallel processing and classification capability of neural networks to produce an efficient implementation of vector quantization. Multi-Stage, tree searched, and classification vector quantization codebook design techniques are adapted to the neural network design to reduce the computational cost and hardware requirements. The results show that the new algorithm provides a substantial reduction in computational costs and an improvement in performance.
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Lewis, Michael. "Data compression for digital elevation models." Thesis, University of South Wales, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265470.
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Darakis, Emmanouil. "Advanced digital holographic data compression methods." Thesis, University of Strathclyde, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438460.
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Reid, Mark Montgomery. "Path-dictated, lossless volumetric data compression." Thesis, University of Ulster, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338194.
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Gooch, Mark. "High performance lossless data compression hardware." Thesis, Loughborough University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338008.
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Nunez, Yanez Jose Luis. "Gbit/second lossless data compression hardware." Thesis, Loughborough University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392516.
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ROSA, JANAINA OLEINIK MOURA. "A STUDY OF BIOSEQUENCE DATA COMPRESSION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9762@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROA família de algoritmos BLAST é a mais utilizada pelos biólogos para a busca de similaridade entre biosseqüências, e por esta razão, melhoras nestes algoritmos, em suas estruturas de dados ou em seus métodos de acesso à memória secundária são muito importantes para o avanço das descobertas biológicas. Nesta dissertação, foi estudada detalhadamente uma versão do programa BLAST, analisando as suas estruturas de dados e os algoritmos que as manipulam. Além disso, foram realizadas medições de desempenho com o intuito de identificar os possíveis gargalos de processamento dentro das fases de execução do BLAST. A partir das informações obtidas, técnicas de compactação de dados foram utilizadas como uma estratégia para redução de acesso à memória secundária com o objetivo de melhorar o desempenho para a execução do BLAST. Finalmente, foi gerada uma versão modificada do BLAST no ambiente Windows, na qual foi alterado diretamente o código do programa. Os resultados obtidos foram comparados com os resultados obtidos na execução do algoritmo original.
The BLAST is the sequence comparison strategy mostly used in computational biology. Therefore, research on data structures, secondary memory access methods and on the algorithm itself, could bring important optimizations and consequently contributions to the area. In this work, we study a NCBI BLAST version by analyzing its data structures and algorithms for data manipulating. In addition, we collect performance data for identifying processing bottleneck in all the BLAST execution phases. Based on this analysis, data compress techniques were applied as a strategy for reducing number of secondary memory access operations. Finally, a modified version of BLAST was implemented in the Microsoft Windows environment, where the program was directly altered. Finally, an analysis was made over using the results of execution of original BLAST against modified BLAST.
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ANJOS, FLAVIA MEDEIROS DOS. "REORGANIZATION AND COMPRESSION OF SEISMIC DATA." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=11337@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICODados sísmicos, utilizados principalmente na indústria de petróleo, costumam apresentar dimensões de dezenas de gigabytes e em alguns casos, centenas. Este trabalho apresenta propostas de manipulação destes dados que ajudem a contornar problemas enfrentados por aplicativos de processamento e interpretação sísmica ao trabalhar com arquivos deste porte. As propostas se baseiam em reorganização e compressão. O conhecimento do formato de utilização dos dados permite reestruturar seu armazenamento diminuindo o tempo gasto com a transferência entre o disco e a memória em até 90%. A compressão é utilizada para diminuir o espaço necessário para armazenamento. Para dados desta natureza os melhores resultados, em taxa de redução, são das técnicas de compressão com perda, entre elas as compressões por agrupamento. Neste trabalho apresentamos um algoritmo que minimiza o erro médio do agrupamento uma vez que o número de grupos tenha sido determinado. Em qualquer método desta categoria o grau de erro e a taxa de compressão obtidos dependem do número de grupos. Os dados sísmicos possuem uma coerência espacial que pode ser aproveitada para melhorar a compressão dos mesmos. Combinando-se agrupamento e o aproveitamento da coerência espacial conseguimos comprimir os dados com taxas variando de 7% a 25% dependendo do erro associado. Um novo formato é proposto utilizando a reorganização e a compressão em conjunto.
Seismic data, used mainly in the petroleum industry, commonly present sizes of tens of gigabyte, and, in some cases, hundreds. This work presents propositions for manipulating these data in order to help overcoming the problems that application for seismic processing and interpretation face while dealing with file of such magnitude. The propositions are based on reorganization and compression. The knowledge of the format in which the data will be used allows us to restructure storage reducing disc- memory transference time up to 90%. Compression is used to save storage space. For data of such nature, best results in terms of compression rates come from techniques associated to information loss, being clustering one of them. In this work we present an algorithm for minimizing the cost of clustering a set of data for a pre-determined number of clusters. Seismic data have space coherence that can be used to improve their compression. Combining clustering with the use of space coherence we were able to compress sets of data with rates from 7% to 25% depending on the error associated. A new file format is proposed using reorganization and compression together.
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Tang, Wang-Rei 1975. "The optimization of data compression algorithms." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/81553.
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Lovén, Johan. "Data Compression in a Vehicular Environment." Thesis, KTH, Signalbehandling, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-155396.
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Vehicles with sensors produce large amounts of data. Transmission of data is connectedwith costs. By using data compression, less data has to be transmitted, reducing priceof operation. A number of algorithms are evaluated through tests with GPS and accelerometerdata. Performance is measured based on time usage, compression ratio,and euclidean errors (in the case of lossy compression). No algorithm or combinationof algorithms is shown to outperform the others on all data and all metrics. Threedierent recommendations are made. For lossless data compression focused on speed, acombination of Move-To-Front, Zero-Run-Length-Encoding, and Human coding is recommended.For lossless data compression focusing on compression ratio, a combinationof delta coding, Lempel-Ziv coding, and Human coding is recommended. Finally, fordata that can aord losses and that has a low amount of unique symbols, a model basedapproach is recommended.
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Smith, Daniel D. "DATA COMPRESSION IN PCM TELEMETRY SYSTEMS." International Foundation for Telemetering, 1986. http://hdl.handle.net/10150/615422.
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International Telemetering Conference Proceedings / October 13-16, 1986 / Riviera Hotel, Las Vegas, NevadaThe data capacity of aerospace PCM telemetry systems can be greatly improved by the application of data compression techniques, which exploit the redundancy in typical telemetry data. This paper describes the design concept for a compressed PCM (CPCM) telemetry system, and contrasts system design considerations to those involved in development of a conventional PCM system. Simulation results are presented depicting potential benefits from use of telemetry data compression on an upper-stage launch vehicle.
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RAJYALAKSHMI, P. S., and R. K. RAJANGAM. "DATA COMPRESSION SYSTEM FOR VIDEO IMAGES." International Foundation for Telemetering, 1986. http://hdl.handle.net/10150/615539.
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International Telemetering Conference Proceedings / October 13-16, 1986 / Riviera Hotel, Las Vegas, NevadaIn most transmission channels, bandwidth is at a premium and an important attribute of any good digital signalling scheme is to optimally utilise the bandwidth for transmitting the information. The Data Compression System in this way plays a significant role in the transmission of picture data from any Remote Sensing Satellite by exploiting the statistical properties of the imagery. The data rate required for transmission to ground can be reduced by using suitable compression technique. A data compression algorithm has been developed for processing the images of Indian Remote Sensing Satellite. Sample LANDSAT imagery and also a reference photo are used for evaluating the performance of the system. The reconstructed images are obtained after compression for 1.5 bits per pixel and 2 bits per pixel as against the original of 7 bits per pixel. The technique used is uni-dimensional Hadamard Transform Technique. The Histograms are computed for various pictures which are used as samples. This paper describes the development of such a hardware and software system and also indicates how hardware can be adopted for a two dimensional Hadamard Transform Technique.
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Toufie, Moegamat Zahir. "Real-time loss-less data compression." Thesis, Cape Technikon, 2000. http://hdl.handle.net/20.500.11838/1367.
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Thesis (MTech (Information Technology))--Cape Technikon, Cape Town, 2000Data stored on disks generally contain significant redundancy. A mechanism or algorithm that recodes the data to lessen the data size could possibly double or triple the effective data that could be stored on the media. One mechanism of doing this is by data compression. Many compression algorithms currently exist, but each one has its own advantages as well as disadvantages. The objective of this study', to formulate a new compression algorithm that could be implemented in a real-time mode in any file system. The new compression algorithm should also execute as fast as possible, so as not to cause a lag in the file systems performance. This study focuses on binary data of any type, whereas previous articles such as (Huftnlan. 1952:1098), (Ziv & Lempel, 1977:337: 1978:530), (Storer & Szymanski. 1982:928) and (Welch, 1984:8) have placed particular emphasis on text compression in their discussions of compression algorithms for computer data. The resulting compression algorithm that is formulated by this study is Lempel-Ziv-Toutlc (LZT). LZT is basically an LZ77 (Ziv & Lempel, 1977:337) encoder with a buffer size equal in size to that of the data block of the file system in question. LZT does not make this distinction, it discards the sliding buffer principle and uses each data block of the entire input stream. as one big buffer on which compression can be performed. LZT also handles the encoding of a match slightly different to that of LZ77. An LZT match is encoded by two bit streams, the first specifying the position of the match and the other specifying the length of the match. This combination is commonly referred to as a
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Lum, Randall M. G. "Differential pulse code modulation data compression." Scholarly Commons, 1989. https://scholarlycommons.pacific.edu/uop_etds/2181.
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With the requirement to store and transmit information efficiently, an ever increasing number of uses of data compression techniques have been generated in diverse fields such as television, surveillance, remote sensing, medical processing, office automation, and robotics. Rapid increases in processing capabilities and the speed of complex integrated circuits make data compression techniques a prime candidate for application in the areas mentioned above. This report addresses, from a theoretical viewpoint, three major data compression techniques, Pixel Coding, Predictive Coding, and Transform Coding. It begins with a project description and continues with data compression techniques, focusing on Differential Pulse Code Modulation.
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Pratas, Diogo. "Compression and analysis of genomic data." Doctoral thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/16286.
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Doutoramento em InformáticaGenomic sequences are large codi ed messages describing most of the structure of all known living organisms. Since the presentation of the rst genomic sequence, a huge amount of genomics data have been generated, with diversi ed characteristics, rendering the data deluge phenomenon a serious problem in most genomics centers. As such, most of the data are discarded (when possible), while other are compressed using general purpose algorithms, often attaining modest data reduction results. Several speci c algorithms have been proposed for the compression of genomic data, but unfortunately only a few of them have been made available as usable and reliable compression tools. From those, most have been developed to some speci c purpose. In this thesis, we propose a compressor for genomic sequences of multiple natures, able to function in a reference or reference-free mode. Besides, it is very exible and can cope with diverse hardware speci cations. It uses a mixture of nite-context models (FCMs) and eXtended FCMs. The results show improvements over state-of-the-art compressors. Since the compressor can be seen as a unsupervised alignment-free method to estimate algorithmic complexity of genomic sequences, it is the ideal candidate to perform analysis of and between sequences. Accordingly, we de ne a way to approximate directly the Normalized Information Distance, aiming to identify evolutionary similarities in intra- and inter-species. Moreover, we introduce a new concept, the Normalized Relative Compression, that is able to quantify and infer new characteristics of the data, previously undetected by other methods. We also investigate local measures, being able to locate speci c events, using complexity pro les. Furthermore, we present and explore a method based on complexity pro les to detect and visualize genomic rearrangements between sequences, identifying several insights of the genomic evolution of humans. Finally, we introduce the concept of relative uniqueness and apply it to the Ebolavirus, identifying three regions that appear in all the virus sequences outbreak but nowhere in the human genome. In fact, we show that these sequences are su cient to classify di erent sub-species. Also, we identify regions in human chromosomes that are absent from close primates DNA, specifying novel traits in human uniqueness.
As sequências genómicas podem ser vistas como grandes mensagens codificadas, descrevendo a maior parte da estrutura de todos os organismos vivos. Desde a apresentação da primeira sequência, um enorme número de dados genómicos tem sido gerado, com diversas características, originando um sério problema de excesso de dados nos principais centros de genómica. Por esta razão, a maioria dos dados é descartada (quando possível), enquanto outros são comprimidos usando algoritmos genéricos, quase sempre obtendo resultados de compressão modestos. Têm também sido propostos alguns algoritmos de compressão para sequências genómicas, mas infelizmente apenas alguns estão disponíveis como ferramentas eficientes e prontas para utilização. Destes, a maioria tem sido utilizada para propósitos específicos. Nesta tese, propomos um compressor para sequências genómicas de natureza múltipla, capaz de funcionar em modo referencial ou sem referência. Além disso, é bastante flexível e pode lidar com diversas especificações de hardware. O compressor usa uma mistura de modelos de contexto-finito (FCMs) e FCMs estendidos. Os resultados mostram melhorias relativamente a compressores estado-dearte. Uma vez que o compressor pode ser visto como um método não supervisionado, que não utiliza alinhamentos para estimar a complexidade algortímica das sequências genómicas, ele é o candidato ideal para realizar análise de e entre sequências. Em conformidade, definimos uma maneira de aproximar directamente a distância de informação normalizada (NID), visando a identificação evolucionária de similaridades em intra e interespécies. Além disso, introduzimos um novo conceito, a compressão relativa normalizada (NRC), que é capaz de quantificar e inferir novas características nos dados, anteriormente indetectados por outros métodos. Investigamos também medidas locais, localizando eventos específicos, usando perfis de complexidade. Propomos e exploramos um novo método baseado em perfis de complexidade para detectar e visualizar rearranjos genómicos entre sequências, identificando algumas características da evolução genómica humana. Por último, introduzimos um novo conceito de singularidade relativa e aplicamo-lo ao Ebolavirus, identificando três regiões presentes em todas as sequências do surto viral, mas ausentes do genoma humano. De facto, mostramos que as três sequências são suficientes para classificar diferentes sub-espécies. Também identificamos regiões nos cromossomas humanos que estão ausentes do ADN de primatas próximos, especificando novas características da singularidade humana.
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Chen, Zhenghao. "Deep Learning for Visual Data Compression." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29729.
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With the tremendous success of neural networks, a few learning-based image codecs were proposed and outperformed those traditional image codecs. However, the field of learning-based compression research for other categories of visual data has remained much less explored. This thesis will investigate the effectiveness of deep learning for visual data compression and propose three end-to-end learning-based compression methods for respectively compressing standard videos, 3D volumetric images and stereo videos.
First, we improve the existing learning-based video codecs by using a newly proposed adaptive coding method called Resolution-adaptive Motion Coding (RaMC) to effectively compress the introduced motion information for reducing the bit-rate cost. Then, we investigate the effectiveness of deep learning for lossless 3D volumetric image compression and propose the first end-to-end optimized learning framework for losslessly compressing 3D volumetric images. We introduce an Intra-slice and Inter-slice Conditional Entropy Coding (ICEC) module to fuse multi-scale intra-slice and inter-slice features as the context information for better entropy coding. Besides the aforementioned single-view visual data, we further attempt to employ the neural networks for compressing the multi-view visual data and propose the first end-to-end Learning-based Stereo Video Compression (LSVC) framework. It compresses both left and right views of the stereo video by using deep motion and disparity compensation strategy with fully-differentiable modules and can be optimized in an end-to-end manner.
We conduct extensive experiments on multiple publicly available datasets to demonstrate the effectiveness of our proposed RaMC, ICEC, and LSVC methods. The results indicate that these three methods achieve state-of-the-art compression performance in the corresponding visual data compression tasks and outperform traditional visual data compression frameworks.
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Pizzolante, Raffaele. "Compression and protection of multidimensional data." Doctoral thesis, Universita degli studi di Salerno, 2015. http://hdl.handle.net/10556/1943.
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2013 - 2014The main objective of this thesis is to explore and discuss novel techniques related to the compression and protection of multidimensional data (i.e., 3-D medical images, hyperspectral images, 3-D microscopy images and 5-D functional Magnetic Resonance Images). First, we outline a lossless compression scheme based on the predictive model, denoted as Medical Images Lossless Compression algorithm (MILC). MILC is characterized to provide a good trade-off between the compression performances and reduced usage of the hardware resources. Since in the medical and medical-related fields, the execution speed of an algorithm, could be a “critical” parameter, we investigate the parallelization of the compression strategy of the MILC algorithm, which is denoted as Parallel MILC. Parallel MILC can be executed on heterogeneous devices (i.e., CPUs, GPUs, etc.) and provides significant results in terms of speedup with respect to the MILC. This is followed by the important aspects related to the protection of two sensitive typologies of multidimensional data: 3-D medical images and 3-D microscopy images. Regarding the protection of 3-D medical images, we outline a novel hybrid approach, which allows for the efficient compression of 3-D medical images as well as the embedding of a digital watermark, at the same time. In relation to the protection of 3-D microscopy images, the simultaneous embedding of two watermarks is explained. It should be noted that 3-D microscopy images are often used in delicate tasks (i.e., forensic analysis, etc.). Subsequently, we review a novel predictive structure that is appropriate for the lossless compression of different typologies of multidimensional data... [edited by Author]
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Lee, Joshua Ka-Wing. "A model-adaptive universal data compression architecture with applications to image compression." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111868.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 59-61).
In this thesis, I designed and implemented a model-adaptive data compression system for the compression of image data. The system is a realization and extension of the Model-Quantizer-Code-Separation Architecture for universal data compression which uses Low-Density-Parity-Check Codes for encoding and probabilistic graphical models and message-passing algorithms for decoding. We implement a lossless bi-level image data compressor as well as a lossy greyscale image compressor and explain how these compressors can rapidly adapt to changes in source models. We then show using these implementations that Restricted Boltzmann Machines are an effective source model for compressing image data compared to other compression methods by comparing compression performance using these source models on various image datasets.
by Joshua Ka-Wing Lee.
S.M.
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Portell, i. de Mora Jordi. "Payload data handling, telemetry and data compression systems for Gaia." Doctoral thesis, Universitat Politècnica de Catalunya, 2005. http://hdl.handle.net/10803/6585.
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Gaia, la nova missió astromètrica de la ESA amb un llançament previst pel 2011, observarà més de mil milions d'estels i altres objectes amb una exactitud sense precedents. Els seus ambiciosos objectius desbanquen completament les missions rivals d'altres agències. Al final de la seva vida útil es generarà el major i més complert mapa tridimensional de la nostra Galàxia.Una missió com aquesta suposa grans esforços tecnològics i de disseny ja que caldrà detectar, seleccionar i mesurar centenars d'estels cada segon, per enviar-ne posteriorment les dades cap a la Terra -a més d'un milió i mig de quilòmetres. Hem centrat el treball d'aquesta tesi en aquesta vessant de la missió, proposant dissenys pels sistemes de gestió de dades, de telemetria científica, i de compressió de dades. El nostre objectiu final és fer possible la transmissió a l'estació terrestre d'aquesta immensa quantitat de dades generades pels instruments, tenint en compte la limitada capacitat del canal de comunicacions. Això requereix el disseny d'un sistema de compressió de dades sense pèrdues que ofereixi les millors relacions de compressió i garanteixi la integritat de les dades transmeses. Tot plegat suposa un gran repte pels mètodes de la teoria de la informació i pel disseny de sistemes de compressió de dades.
Aquests aspectes tecnològics encara estaven per estudiar o bé només es disposava d'esborranys preliminars -ja que la missió mateixa estava en una etapa preliminar en quan varem començar aquesta tesi. Per tant, el nostre treball ha estat rebut amb entusiasme per part de científics i enginyers del projecte.
En primer lloc hem revisat l'entorn operacional del nostre estudi, descrit a la primera part de la tesi. Això inclou els diversos sistemes de referència i les convencions que hem proposat per tal d'unificar les mesures, referències a dades i dissenys. Aquesta proposta s'ha utilitzat com a referència inicial en la missió i actualment altres científics l'estan ampliant i millorant. També hem recopilat les principals característiques de l'instrument astromètric (en el qual hem centrat el nostre estudi) i revisat les seves directrius operacionals, la qual cosa també s'ha tingut en compte en altres equips.
A la segona part de la tesi descrivim la nostra proposta pel sistema de gestió de dades de la càrrega útil de Gaia, la qual ha estat utilitzada per presentar els requeriments científics als equips industrials i representa en sí mateixa una opció d'implementació viable (tot i que simplificada). En la següent part estudiem la telemetria científica, recopilant els camps de dades a generar pels instruments i proposant un esquema optimitzat de codificació i transmissió, el qual redueix la ocupació del canal de comunicacions i està preparat per incloure un sistema optimitzat de compressió de dades. Aquest darrer serà descrit a la quarta i última part de la tesi, on veurem com la nostra proposta compleix gairebé totalment els requeriments de compressió, arribant a duplicar les relacions de compressió ofertes pels millors sistemes estàndard. El nostre disseny representa la millor solució actualment disponible per Gaia i el seu rendiment ha estat assumit com a disseny base per altres equips.
Cal dir que els resultats del nostre treball van més enllà de la publicació d'una memòria de tesi, complementant-la amb aplicacions de software que hem desenvolupat per ajudar-nos a dissenyar, optimitzar i verificar la operació dels sistemes aquí proposats. També cal indicar que la complexitat del nostre treball ha estat augmentada degut a la necessitat d'actualitzar-lo contínuament als canvis que la missió ha sofert en el seu disseny durant els cinc anys del doctorat. Per acabar, podem dir que estem satisfets amb els resultats del nostre treball, ja que la majoria han estat (o estan essent) tinguts en compte per molts equips involucrats en la missió i per la mateixa Agència Espacial Europea en el disseny final.
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Horan, Sheila B. "CURRENT STATUS OF DATA COMPRESSION IN TELEMETRY." International Foundation for Telemetering, 2004. http://hdl.handle.net/10150/605065.
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International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, CaliforniaReduction of bandwidth for signal transmission is of paramount concern to many in the telemetry and wireless industry. One way to reduce bandwidth is to reduce the amount data being sent. There are several techniques available to reduce the amount of data. This paper will review the various types of data compression currently in use for telemetry data and how much compression is achieved.
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Aydinoğlu, Behçet Halûk. "Stereo image compression." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/15447.
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Jones, Greg 1963-2017. "RADIX 95n: Binary-to-Text Data Conversion." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc500582/.
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This paper presents Radix 95n, a binary to text data conversion algorithm. Radix 95n (base 95) is a variable length encoding scheme that offers slightly better efficiency than is available with conventional fixed length encoding procedures. Radix 95n advances previous techniques by allowing a greater pool of 7-bit combinations to be made available for 8-bit data translation. Since 8-bit data (i.e. binary files) can prove to be difficult to transfer over 7-bit networks, the Radix 95n conversion technique provides a way to convert data such as compiled programs or graphic images to printable ASCII characters and allows for their transfer over 7-bit networks.
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Haugen, Daniel. "Seismic Data Compression and GPU Memory Latency." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9973.
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The gap between processing performance and the memory bandwidth is still increasing. To compensate for this gap various techniques have been used, such as using a memory hierarchy with faster memory closer to the processing unit. Other techniques that have been tested include the compression of data prior to a memory transfer. Bandwidth limitations exists not only at low levels within the memory hierarchy, but also between the central processing unit (CPU) and the graphics processing unit (GPU), suggesting the use of compression to mask the gap. Seismic datasets are often very large, e.g. several terabytes. This thesis explores compression of seismic data to hide the bandwidth limitation between the CPU and the GPU for seismic applications. The compression method considered is subband coding, with both run-length encoding (RLE) and Huffman encoding as compressors of the quantized data. These methods has shown on CPU implementations to give very good compression ratios for seismic data. A proof of concept implementation for decompression of seismic data on GPUs is developed. It consists of three main components: First the subband synthesis filter reconstructing the input data processed by the subband analysis filter. Second, the inverse quantizer generating an output close to the input given to the quantizer. Finally, the decoders decompressing the compressed data using Huffman and RLE. The results of our implementation show that the seismic data compression algorithm investigated is probably not suited to hide the bandwidth limitation between CPU and GPU. This is because of the steps taken to do the decompression are likely slower than a simple memory copy of the uncompressed seismic data. It is primarily the decompressors that are the limiting factor, but in our implementation the subband synthesis is also limiting. The sequential nature of the decompres- sion algorithms used makes them difficult to parallelize to make use of the processing units on the GPUs in an efficient way. Several suggestions for future work is then suggested as well as results showing how our GPU implementation can be very useful for data compres- sion for data to be sent over a network. Our compression results give a compression factor between 27 and 32, and a SNR of 24.67dB for a cube of dimension 643. A speedup of 2.5 for the synthesis filter compared to the CPU implementation is achieved (2029.00/813.76 2.5). Although not currently suited for the GPU-CPU compression, our implementations indicate
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Aqrawi, Ahmed Adnan. "Effects of Compression on Data Intensive Algorithms." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11797.
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In recent years, the gap between bandwidth and computational throughput has become a major challenge in high performance computing (HPC). Data intensive algorithms are particularly affected. by the limitations of I/O bandwidth and latency. In this thesis project, data compression is explored so that fewer bytes need to be read from disk. The computational capabilities of the GPU are then utilized for faster decompression. Seismic filtering algorithms, which are known to be very data intensive, are used as tests cases. In the thesis, both lossless and lossy compression algorithms are considered,. We have developed, optimized and implemented several compression algorithms for both the CPU and GPU using C, OpenMP and NVIDIA CUDA. A scheme for utilizing both the CPU and GPU using asynchronous I/O to further improve performance is also developed. Compression algorithms studied and optimized include RLE, Huffman encoding, 1D-3D DCT, 1D-3D Fast DCT AAN algorithm, and the fast LOT. 3D convolution and the Hough transform filtering algorithms are also developed and optimized.Lossy compression algorithms using transform encoding are also studied. Using these transforms for compression include: 1) transformation, 2) quantization and 3) encoding. Transformation and quantization are shown to be especially suitable for the GPU because of their parallelizable nature. The encoding step is shown to be best done on the CPU because of its sequential nature. GPU and CPU are used in asynchronous co-operation to perform the compression on seismic data sizes (up to 32GB). Transform coding is lossy, but the errors we experience are minimally visible and are within acceptable loss given the type of data (a max. of 0.46% ME and 81 rMSE for our seismic data sets).HDD disk with 70MB/s transfer rate, and a speedup of 3.3 for a modern SSD with a 140MB/s transfer rate. Several other results on both the recent NVIDIA Tesla c1060 GPU and the new NVIDIA Tesla c2050 Fermi-based GPU, as well as results for using CPU and GPU together using asynchronous I/O is included. The major bottleneck now is the PCI express bus limitations, and for files that do not compress well, the I/O bandwidth and latency is still an issue.
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Olsson, Tom. "Baking And Compression For Dynamic Lighting Data." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-45438.
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This report describes the development and prototype implementation of a method for baking and compression of lightmaps, in an environment with dynamic lights. The method described can achieve more than 95 % compression eciency, and can be easily tuned with only two parameters. Even without specic tuning, the prototype consistently achieves signalto- noise ratios above 30 dB, reaching 60 dB in some scenes. Compression is achieved in four steps, rst by using image segmentation and function approximation to reduce the data-size and then using a predictive quantizer approach based on the PNG-lters together with an open-source compression algorithm. Both compression and decompression can be adapted for asynchronous and multi-threaded execution.Denna report beskriver utvecklingen av en metod for bakning och komprimering av dynamiska ljuskartor, i renderingar med dynamiska ljuskallor. Metoden uppnar mer an 95 % storleksreduktion, och kan enkelt anpassas for olika ljuskartor med tva variabler. Aven utan speci- ka anpassningar uppnas en signal-to-noise niva over 30 dB, och narmare 60 dB i vissa scener. Komprimering sker i fyra steg, forst genom bildsegmentering och linjar funktionsapproximation, foljt av predictive quantization och en vanlig komprimeringsalgorithm. Bade komprimering och dekomprimering kan anpassas for asynkron och ertradig exekvering.
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Alkharabsheh, Khaled S. "Image compression and its effect on data." Huntington, WV : [Marshall University Libraries], 2004. http://www.marshall.edu/etd/descript.asp?ref=462.
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Khanna, Rajiv. "Image data compression using multiple bases representation." Thesis, This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-12302008-063722/.
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