Relevant bibliographies by topics / Image compression

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Image compression'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Image compression"

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Saudagar, Abdul Khader Jilani. "Biomedical Image Compression Techniques for Clinical Image Processing." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 12 (October 19, 2020): 133. http://dx.doi.org/10.3991/ijoe.v16i12.17019.

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Image processing is widely used in the domain of biomedical engineering especially for compression of clinical images. Clinical diagnosis receives high importance which involves handling patient’s data more accurately and wisely when treating patients remotely. Many researchers proposed different methods for compression of medical images using Artificial Intelligence techniques. Developing efficient automated systems for compression of medical images in telemedicine is the focal point in this paper. Three major approaches were proposed here for medical image compression. They are image compression using neural network, fuzzy logic and neuro-fuzzy logic to preserve higher spectral representation to maintain finer edge information’s, and relational coding for inter band coefficients to achieve high compressions. The developed image coding model is evaluated over various quality factors. From the simulation results it is observed that the proposed image coding system can achieve efficient compression performance compared with existing block coding and JPEG coding approaches, even under resource constraint environments.
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Khan, Sulaiman, Shah Nazir, Anwar Hussain, Amjad Ali, and Ayaz Ullah. "An efficient JPEG image compression based on Haar wavelet transform, discrete cosine transform, and run length encoding techniques for advanced manufacturing processes." Measurement and Control 52, no. 9-10 (October 19, 2019): 1532–44. http://dx.doi.org/10.1177/0020294019877508.

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Image compression plays a key role in the transmission of an image and storage capacity. Image compression aims to reduce the size of the image with no loss of significant information and no loss of quality in the image. To reduce the storage capacity of the image, the image compression is proposed in order to offer a compact illustration of the information included in the image. Image compression exists in the form of lossy or lossless. Even though image compression mechanism has a prominent role for compressing images, certain conflicts still exist in the available techniques. This paper presents an approach of Haar wavelet transform, discrete cosine transforms, and run length encoding techniques for advanced manufacturing processes with high image compression rates. These techniques work by converting an image (signal) into half of its length which is known as “detail levels”; then, the compression process is done. For simulation purposes of the proposed research, the images are segmented into 8 × 8 blocks and then inversed (decoded) operation is performed on the processed 8 × 8 block to reconstruct the original image. The same experiments were done on two other algorithms, that is, discrete cosine transform and run length encoding schemes. The proposed system is tested by comparing the results of all the three algorithms based on different images. The comparison among these techniques is drawn on the basis of peak signal to noise ratio and compression ratio. The results obtained from the experiments show that the Haar wavelet transform outperforms very well with an accuracy of 97.8% and speeds up the compression and decompression process of the image with no loss of information and quality of image. The proposed study can easily be implemented in industries for the compression of images. These compressed images are suggested for multiple purposes like image compression for metrology as measurement materials in advanced manufacturing processes, low storage and bandwidth requirements, and compressing multimedia data like audio and video formats.
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David S, Alex, Almas Begum, and Ravikumar S. "Content clustering for MRI Image compression using PPAM." International Journal of Engineering & Technology 7, no. 1.7 (February 5, 2018): 126. http://dx.doi.org/10.14419/ijet.v7i1.7.10631.

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Image compression helps to save the utilization of memory, data while transferring the images between nodes. Compression is one of the key technique in medical image. Both lossy and lossless compressions where used based on the application. In case of medical imaging each and every components of pixel is very important hence its nature to chose lossless compression medical images. MRI images are compressed after processing. Here in this paper we have used PPMA method to compress the MRI image. For retrieval of the compressed image content clustering method used.
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Katayama, O., S. Ishihama, K. Namiki, and I. Ohi. "Color Changes in Electronic Endoscopic Images Caused by Image Compression." Diagnostic and Therapeutic Endoscopy 4, no. 1 (January 1, 1997): 43–50. http://dx.doi.org/10.1155/dte.4.43.

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In recent years, recording of color still images into magneto–optical video disks has been increasingly used as a method for recording electronic endoscopic images. In this case, image compression is often used to reduce the volume and cost of recording media and also to minimize the time required for image recording and playback. With this in mind, we recorded 8 images into a magneto-optical video disk in 4 image compression modes (no compression, weak compression, moderate compression, and strong compression) using the Joint Photographic Image Coding Experts Group (JPEG) system, which is a widely used and representative method for compressing color still images, in order to determine the relationship between the degree of image compression and the color information in electronic endoscopic images. The acquired images were transferred to an image processor using an offline system. A total of 10 regions of interest (ROls) were selected, and red (R), green (G), and blue (B) images were obtained using different compression modes. From histograms generated for these images, mean densities of R, G, and B in each ROI were measured and analyzed. The results revealed that color changes were greater for B, which had the lowest density, than for R or G as the degree of compression was increased.
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Khatun, Shamina, and Anas Iqbal. "A Review of Image Compression Using Fractal Image Compression with Neural Network." International Journal of Innovative Research in Computer Science & Technology 6, no. 2 (March 31, 2018): 9–11. http://dx.doi.org/10.21276/ijircst.2018.6.2.1.

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Kaur, Gaganpreet, Hitashi Hitashi, and Dr Gurdev Singh. "PERFORMANCE EVALUATION OF IMAGE QUALITY BASED ON FRACTAL IMAGE COMPRESSION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 1 (February 2, 2012): 20–27. http://dx.doi.org/10.24297/ijct.v2i1.2608.

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Fractal techniques for image compression haverecently attracted a great deal of attention. Fractalimage compression is a relatively recenttechnique based on the representation of animage by a contractive transform, on the space ofimages, for which the fixed point is close to theoriginal image. This broad principle encompassesa very wide variety of coding schemes, many ofwhich have been explored in the rapidly growingbody of published research.Unfortunately, littlein the way of practical algorithms or techniqueshas been published. Here present a technique forimage compression that is based on a very simpletype of iterative fractal. In our algorithm awavelet transform (quadrature mirror filterpyramid) is used to decompose an image intobands containing information from differentscales (spatial frequencies) and orientations. Theconditional probabilities between these differentscale bands are then determined, and used as thebasis for a predictive coder.We undertake a study of the performance offractal image compression. This paper focusesimportant features of compression of still images,including the extent to which the quality of imageis degraded by the process of compression anddecompression.The numerical experiment is doneby considering various types of images and byapplying fractal Image compression to compressan image. It was found that fractal yields betterresult as compared to other compressiontechniques. It provide better peak signal to noiseratio as compare to other techniques, but it takehigher encoding time.The numerical results arecalculated in Matlab.
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Cardone, Barbara, Ferdinando Di Martino, and Salvatore Sessa. "Fuzzy Transform Image Compression in the YUV Space." Computation 11, no. 10 (October 1, 2023): 191. http://dx.doi.org/10.3390/computation11100191.

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This research proposes a new image compression method based on the F1-transform which improves the quality of the reconstructed image without increasing the coding/decoding CPU time. The advantage of compressing color images in the YUV space is due to the fact that while the three bands Red, Green and Blue are equally perceived by the human eye, in YUV space most of the image information perceived by the human eye is contained in the Y band, as opposed to the U and V bands. Using this advantage, we construct a new color image compression algorithm based on F1-transform in which the image compression is accomplished in the YUV space, so that better-quality compressed images can be obtained without increasing the execution time. The results of tests performed on a set of color images show that our color image compression method improves the quality of the decoded images with respect to the image compression algorithms JPEG, F1-transform on the RGB color space and F-transform on the YUV color space, regardless of the selected compression rate and with comparable CPU times.
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Mohammed, Hind Rostom, and Ameer Abd Al-Razaq. "SWF Image Compression by Evaluating objects compression ratio." Journal of Kufa for Mathematics and Computer 1, no. 2 (October 30, 2010): 105–18. http://dx.doi.org/10.31642/jokmc/2018/010209.

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This work discusses the compression objects ratio for Macromedia Flash File (SWF) Image by Wavelet functions for compression and there effect for Macromedia Flash File (SWF) Images compression . We discusses classification objects in Macromedia Flash (SWF) image in to nine types objects Action, Font,Image, Sound, Text, Button, Frame, Shape and Sprite. The work is particularly targeted towards wavelet image compression best case by using Haar Wavelet Transformation with an idea to minimize the computational requirements by applying different compression thresholds for the waveletcoefficients and these results are obtained in fraction of seconds and thus to improve thequality of the reconstructed image. The promising results obtained concerning reconstructed images quality as well as preservation of significant image details, while, on the other hand achieving highcompression rates and better image quality while DB4 Wavelet Transformation higher compression rates ratio without kept for image quality .
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Paul, Okuwobi Idowu, and Yong Hua Lu. "A New Approach in Digital Image Compression Using Unequal Error Protection (UEP)." Applied Mechanics and Materials 704 (December 2014): 403–7. http://dx.doi.org/10.4028/www.scientific.net/amm.704.403.

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This paper proposes a new algorithms for compression of digital images especially at the encoding stage of compressive sensing. The research consider the fact that a certain region of a given imagery is more important in most applications. The first algorithm proposed for the encoding stage of Compressive Sensing (CS) exploits the known structure of transform image coefficients. The proposed algorithm makes use of the unequal error protection (UEP) principle, which is widely used in the area of error control coding. The second algorithm which exploits the UEP principle to recover the more important part of an image with more quality while the rest part of the image is not significantly degraded. The proposed algorithm shown to be successful in digital image compression where images are represented in the spatial and transform domains. This new algorithm were recommended for use in image compression.
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Mohammed, Sajaa G., Safa S. Abdul-Jabbar, and Faisel G. Mohammed. "Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm." Journal of Physics: Conference Series 2114, no. 1 (December 1, 2021): 012080. http://dx.doi.org/10.1088/1742-6596/2114/1/012080.

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Abstract Color image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm Welch LZW. The proposed methodology maintains the quality of the reconstructed image. Macroscopic and quantitative experimental results on technical color images show that the proposed methodology gives reconstructed images with a high PSNR value compared to standard image compression techniques.
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Dissertations / Theses on the topic "Image compression"

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Hawary, Fatma. "Light field image compression and compressive acquisition." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S082.

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En capturant une scène à partir de plusieurs points de vue, un champ de lumière fournit une représentation riche de la géométrie de la scène, ce qui permet une variété de nouvelles applications de post-capture ainsi que des expériences immersives. L'objectif de cette thèse est d'étudier la compressibilité des contenus de type champ de lumière afin de proposer de nouvelles solutions pour une imagerie de champs lumière à plus haute résolution. Deux aspects principaux ont été étudiés à travers ce travail. Les performances en compression sur les champs lumière des schémas de codage actuels étant encore limitées, il est nécessaire d'introduire des approches plus adaptées aux structures des champs de lumière. Nous proposons un schéma de compression comportant deux couches de codage. Une première couche encode uniquement un sous-ensemble de vues d’un champ de lumière et reconstruit les vues restantes via une méthode basée sur la parcimonie. Un codage résiduel améliore ensuite la qualité finale du champ de lumière décodé. Avec les moyens actuels de capture et de stockage, l’acquisition d’un champ de lumière à très haute résolution spatiale et angulaire reste impossible, une alternative consiste à reconstruire le champ de lumière avec une large résolution à partir d’un sous-ensemble d’échantillons acquis. Nous proposons une méthode de reconstruction automatique pour restaurer un champ de lumière échantillonné. L’approche utilise la parcimonie du champs de lumière dans le domaine de Fourier. Aucune estimation de la géométrie de la scène n'est nécessaire, et une reconstruction précise est obtenue même avec un échantillonnage assez réduit. Une étude supplémentaire du schéma complet, comprenant les deux approches proposées est menée afin de mesurer la distorsion introduite par les différents traitements. Les résultats montrent des performances comparables aux méthodes de synthèse de vues basées sur la l’estimation de profondeur
By capturing a scene from several points of view, a light field provides a rich representation of the scene geometry that brings a variety of novel post-capture applications and enables immersive experiences. The objective of this thesis is to study the compressibility of light field contents in order to propose novel solutions for higher-resolution light field imaging. Two main aspects were studied through this work. The compression performance on light fields of the actual coding schemes still being limited, there is need to introduce more adapted approaches to better describe the light field structures. We propose a scalable coding scheme that encodes only a subset of light field views and reconstruct the remaining views via a sparsity-based method. A residual coding provides an enhancement to the final quality of the decoded light field. Acquiring very large-scale light fields is still not feasible with the actual capture and storage facilities, a possible alternative is to reconstruct the densely sampled light field from a subset of acquired samples. We propose an automatic reconstruction method to recover a compressively sampled light field, that exploits its sparsity in the Fourier domain. No geometry estimation is needed, and an accurate reconstruction is achieved even with very low number of captured samples. A further study is conducted for the full scheme including a compressive sensing of a light field and its transmission via the proposed coding approach. The distortion introduced by the different processing is measured. The results show comparable performances to depth-based view synthesis methods
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Obaid, Arif. "Range image compression." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/10131.

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Range Images, which are a representation of the surface of a 3-D object, are gaining popularity in many applications including CAD/CAM, multimedia and virtual reality. There is, thus, a need for compression of these 3-D images. Current standards for still image compression, such as JPEG, are not appropriate for such images because they have been designed specifically for intensity images. This has led us to develop a new compression method for range images. It first scans the image so that the pixels are arranged into a sequence. It then approximates this sequence by straight line segments within a user-specified maximum tolerance level. The extremities of the straight-line segments within a user-specified maximum tolerance level. The extremities of the straight-line segments are non-redundant points (NRPs). Huffman coding, with a fixed Huffman tree, is used to encode the distance between NRPs and their altitudes. A plane-filling scanning technique, known as Peano scanning, is used to improve performance. The algorithms performance is assessed on range images acquired from the Institute for Information Technology of the National Research Council of Canada. The proposed method performs better than JPEG for any given maximum tolerance level. The adaptive mode of the algorithm is also presented along with its performance assessment.
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Lacroix, Bruno. "Fractal image compression." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ36939.pdf.

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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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Abdul-Amir, Said. "Digital image compression." Thesis, De Montfort University, 1985. http://hdl.handle.net/2086/10681.

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Due to the rapid growth in information handling and transmission, there is a serious demand for more efficient data compression schemes. compression schemes address themselves to speech, visual and alphanumeric coded data. This thesis is concerned with the compression of visual data given in the form of still or moving pictures. such data is highly correlated spatially and in the context domain. A detailed study of some existing data compression systems is presented, in particular, the performance of DPCM was analysed by computer simulation, and the results examined both subjectively and objectively. The adaptive form of the prediction encoder is discussed and two new algorithms proposed, which increase the definition of the compressed image and reduce the overall mean square error. Two novel systems are proposed for image compression. The first is a bit plane image coding system based on a hierarchic quadtree structure in a transmission domain, using the Hadamard transform as a kernel. Good compression has been achieved from this scheme, particularly for images with low detail. The second scheme uses a learning automata to predict the probability distribution of the grey levels of an image related to its spatial context and position. An optimal reward/punishment function is proposed such that the automata converges to its steady state within 4000 iterations • such a high speed of convergence together with Huffman coding results in efficient compression for images and is shown to be applicable to other types of data. . The performance and evaluation of all the proposed .'systems have been tested by computer simulation and the results presented both quantitatively and qualitatively."The advantages and disadvantages of each system are discussed and suggestions for improvement. given.
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Hallidy, William H. Jr, and Michael Doerr. "HYPERSPECTRAL IMAGE COMPRESSION." International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/608744.

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International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada
Systems & Processes Engineering Corporation (SPEC) compared compression and decompression algorithms and developed optimal forms of lossless and lossy compression for hyperspectral data. We examined the relationship between compression-induced distortion and additive noise, determined the effect of errors on the compressed data, and showed that the data could separate targets from clutter after more than 50:1 compression.
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Hernández-Cabronero, Miguel. "DNA Microarray Image Compression." Doctoral thesis, Universitat Autònoma de Barcelona, 2015. http://hdl.handle.net/10803/297706.

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En los experimentos con DNA microarrays se genran dos imágenes monocromo, las cuales es conveniente almacenar para poder realizar análisis más precisos en un futuro. Por tanto, la compresión de imágenes surge como una herramienta particularmente útil para minimizar los costes asociados al almacenamiento y la transmisión de dichas imágenes. Esta tesis tiene por objetivo mejorar el estado del arte en la compresión de imágenes de DNA microarrays. Como parte de esta tesis, se ha realizado una detallada investigación de las características de las imágenes de DNA microarray. Los resultados experimentales indican que los algoritmos de compresión no adaptados a este tipo de imágenes producen resultados más bien pobres debido a las características de estas imágenes. Analizando las entropías de primer orden y condicionales, se ha podido determinar un límite aproximado a la compresibilidad sin pérdida de estas imágenes. Aunque la compresión basada en contexto y en segmentación proporcionan mejoras modestas frente a algoritmos de compresión genéricos, parece necesario realizar avances rompedores en el campo de compresión de datos para superar los ratios 2:1 en la mayor parte de las imágenes. Antes del comienzo de esta tesis se habían propuesto varios algoritmos de compresión sin pérdida con rendimientos cercanos al límite óptimo anteriormente mencionado. Sin embargo, ninguno es compatible con los estándares de compresión existentes. Por tanto, la disponibilidad de descompresores compatibles en plataformas futuras no está garantizado. Además, la adhesión a dichos estándares se require normalmente en escenarios clínicos. Para abordar estos problemos, se propone una transformada reversible compatible con el standard JPEG2000: la Histogram Swap Transform (HST). La HST mejora el rendimiento medio de JPEG2000 en todos los corpora entre 1.97% y 15.53%. Además, esta transformada puede aplicarse incurriendo en un sobrecoste de tiempo negligible. Con la HST, JPEG2000 se convierte en la alternativa estándard más competitiva a los compresores no estándard. Las similaridades entre imágenes del mismo corpus también se han estudiado para mejorar aún más los resultados de compresión de imágenes de DNA microarrays. En concreto, se ha encontrado una agrupación óptima de las imágenes que maximiza la correlación dentro de los grupos. Dependiendo del corpus observado, pueden observarse resultados de correlación medios de entre 0.75 y 0.92. Los resultados experimentales obtenidos indican que las técnicas de decorrelación espectral pueden mejorar los resultados de compresión hasta en 0.6 bpp, si bien ninguna de las transformadas es efectiva para todos los corpora utilizados. Por otro lado, los algoritmos de compresión con pérdida permiten obtener resultados de compresión arbitrarios a cambio de modificar las imágenes y, por tanto, de distorsionar subsiguientes procesos de análisis. Si la distorsión introducida es más pequeña que la variabilidad experimental inherente, dicha distorsión se considera generalmente aceptable. Por tanto, el uso de técnicas de compresión con pérdida está justificado. En esta tesis se propone una métrica de distorsión para imágenes de DNA microarrays capaz de predecir la cantidad de distorsión introducida en el análisis sin necesitar analizar las imágenes modificadas, diferenciando entre cambios importantes y no importantes. Asimismo, aunque ya se habían propuesto algunos algoritmos de compresión con pérdida para estas imágenes antes del comienzo de la tesis, ninguno estaba específicamente diseñado para minimizar el impacto en los procesos de análisis para un bitrate prefijado. En esta tesis, se propone un compresor con pérdida (el Relative Quantizer (RQ) coder) que mejora los resultados de todos los métodos anteriormente publicados. Los resultados obtenidos sugieren que es posible comprimir con ratios superiores a 4.5:1 mientras se introducen distorsiones en el análisis inferiores a la mitad de la variabilidad experimental inherente. Además, se han propuesto algunas mejoras a dicho compresor, las cuales permiten realizar una codificación lossy-to-lossless (el Progressive RQ (PRQ) coder), pudiéndose así reconstruir una imagen comprimida con diferentes niveles de calidad. Cabe señalar que los resultados de compresión anteriormente mencionados se obtienen con una complejidad computacional ligeramente inferior a la del mejor compresor sin pérdida para imágenes de DNA microarrays.
In DNA microarray experiments, two grayscale images are produced. It is convenient to save these images for future, more accurate re-analysis. Thus, image compression emerges as a particularly useful tool to alleviate the associated storage and transmission costs. This dissertation aims at improving the state of the art of the compression of DNA microarray images. A thorough investigation of the characteristics of DNA microarray images has been performed as a part of this work. Results indicate that algorithms not adapted to DNA microarray images typically attain only mediocre lossless compression results due to the image characteristics. By analyzing the first-order and conditional entropy present in these images, it is possible to determine approximate limits to their lossless compressibility. Even though context-based coding and segmentation provide modest improvements over generic-purpose algorithms, conceptual breakthroughs in data coding are arguably required to achieve compression ratios exceeding 2:1 for most images. Prior to the start of this thesis, several lossless coding algorithms that have performance results close to the aforementioned limit were published. However, none of them is compliant with existing image compression standards. Hence, the availability of decoders in future platforms -a requisite for future re-analysis- is not guaranteed. Moreover, the adhesion to standards is usually a requisite in clinical scenarios. To address these problems, a fast reversible transform compatible with the JPEG2000 standard -the Histogram Swap Transform (HST)- is proposed. The HST improves the average compression performance of JPEG2000 for all tested image corpora, with gains ranging from 1.97% to 15.53%. Furthermore, this transform can be applied with only negligible time complexity overhead. With the HST, JPEG2000 becomes arguably the most competitive alternatives to microarray-specific, non-standard compressors. The similarities among sets of microarray images have also been studied as a means to improve the compression performance of standard and microarray-specific algorithms. An optimal grouping of the images which maximizes the inter-group correlation is described. Average correlations between 0.75 and 0.92 are observed for the tested corpora. Thorough experimental results suggest that spectral decorrelation transforms can improve some lossless coding results by up to 0.6bpp, although no single transform is effective for all copora. Lossy coding algorithms can yield almost arbitrary compression ratios at the cost of modifying the images and, thus, of distorting subsequent analysis processes. If the introduced distortion is smaller than the inherent experimental variability, it is usually considered acceptable. Hence, the use of lossy compression is justified on the assumption that the analysis distortion is assessed. In this work, a distortion metric for DNA microarray images is proposed to predict the extent of this distortion without needing a complete re-analysis of the modified images. Experimental results suggest that this metric is able to tell apart image changes that affect subsequent analysis from image modifications that do not. Although some lossy coding algorithms were previously described for this type of images, none of them is specifically designed to minimize the impact on subsequent analysis for a given target bitrate. In this dissertation, a lossy coder -the Relative Quantizer (RQ) coder- that improves upon the rate- distortion results of previously published methods is proposed. Experiments suggest that compression ratios exceeding 4.5:1 can be achieved while introducing distortions smaller than half the inherent experimental variability. Furthermore, a lossy-to-lossless extension of this coder -the Progressive RQ (PRQ) coder- is also described. With the PRQ, images can be compressed once and then reconstructed at different quality levels, including lossless reconstruction. In addition, the competitive rate-distortion results of the RQ and PRQ coders can be obtained with computational complexity slightly smaller than that of the best-performing lossless coder of DNA microarray images.
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Agostini, Luciano Volcan. "Projeto de arquiteturas integradas para a compressão de imagens JPEG." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2002. http://hdl.handle.net/10183/11431.

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Esta dissertação apresenta o desenvolvimento de arquiteturas para a compressão JPEG, onde são apresentadas arquiteturas de um compressor JPEG para imagens em tons de cinza, de um compressor JPEG para imagens coloridas e de um conversor de espaço de cores de RGB para YCbCr. As arquiteturas desenvolvidas são detalhadamente apresentadas, tendo sido completamente descritas em VHDL, com sua síntese direcionada para FPGAs da família Flex10KE da Altera. A arquitetura integrada do compressor JPEG para imagens em tons de cinza possui uma latência mínima de 237 ciclos de clock e processa uma imagem de 640x480 pixels em 18,5ms, permitindo uma taxa de processamento de 54 imagens por segundo. As estimativas realizadas em torno da taxa de compressão obtida indicam que ela seria de aproximadamente 6,2 vezes ou de 84 %. A arquitetura integrada do compressor JPEG para imagens coloridas foi gerada a partir de adaptações na arquitetura do compressor para imagens em tons de cinza. Esta arquitetura também possui a latência mínima de 237 ciclos de clock, sendo capaz de processar uma imagem coloria de 640 x 480 pixels em 54,4ms, permitindo uma taxa de processamento de 18,4 imagens por segundo. A taxa de compressão obtida, segundo estimativas, seria de aproximadamente 14,4 vezes ou de 93 %. A arquitetura para o conversor de espaço de cores de RBG para YCbCr possui uma latência de 6 ciclos de clock e é capaz de processar uma imagem colorida de 640x480 pixels em 84,6ms, o que permite uma taxa de processamento de 11,8 imagens por segundo. Esta arquitetura não chegou a ser integrada com a arquitetura do compressor de imagens coloridas, mas algumas sugestões e estimativas foram realizadas nesta direção.
This dissertation presents the design of architectures for JPEG image compression. Architectures for a gray scale images JPEG compressor that were developed are herein presented. This work also addresses a color images JPEG compressor and a color space converter. The designed architectures are described in detail and they were completely described in VHDL, with synthesis directed for Altera Flex10KE family of FPGAs. The integrated architecture for gray scale images JPEG compressor has a minimum latency of 237 clock cycles and it processes an image of 640x480 pixels in 18,5ms, allowing a processing rate of 54 images per second. The compression rate, according to estimates, would be of 6,2 times or 84%, in percentage of bits compression. The integrated architecture for color images JPEG compression was generated starting from incremental changes in the architecture of gray scale images compressor. This architecture also has the minimum latency of 237 clock cycles and it can process a color image of 640 x 480 pixels in 54,4ms, allowing a processing rate of 18,4 images per second. The compression rate, according to estimates, would be of 14,4 times or 93%, in percentage of bits compression. The architecture for space color conversor from RBG to YCbCr has a latency of 6 clock cycles and it is able to process a color image of 640 x 480 pixels in 84,6ms, allowing a processing rate of 11,8 images per second. This architecture was finally not integrated with the color images compressor architecture, but some suggestions, alternatives and estimates were made in this direction.
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9

Nicholl, Peter Nigel. "Feature directed spiral image compression : (a new technique for lossless image compression)." Thesis, University of Ulster, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339326.

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Mandal, Mrinal Kumar. "Wavelets for image compression." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/10277.

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Wavelets are becoming increasingly important in image compression applications because of its flexibility in representing nonstationary signals. To achieve a high compression ratio, the wavelet has to be adapted to the image. Current techniques use exhaustive search procedures which are computationally intensive to find the optimal basis (type/order/tree) for the image to be coded. In this thesis, we have carried out extensive performance analysis of various wavelets on a wide variety of images. Based on the investigation, we propose some guidelines for searching for the optimal wavelet (type/order) based on the overall activity (measured by the spectral flatness) of the image to be coded. These guidelines will provide the degree of improvement that can be achieved by using the "optimal" over "standard" wavelets. The proposed guidelines can be used to find a good initial guess for faster convergence when searching for optimal wavelet is essential. We propose a wave packet decomposition algorithm based on the local transform gain of the wavelet decomposed bands. The proposed algorithm provides good coding performance at significantly reduced complexity. Most practical coders are designed to minimize the mean square error (MSE) between the original and reconstructed image. It is known that at high compression ratio, MSE does not correspond well to the subjective quality of the image. In this thesis, we propose an image adaptive coding algorithm which tries to minimize the MSE weighted by the visual importance of various wavelet bands. It has been observed that the proposed algorithm provides a better coding performance for a wide variety of images.
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Books on the topic "Image compression"

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Pearlman, William A. Wavelet Image Compression. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-031-02248-7.

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Kou, Weidong. Digital Image Compression. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2361-8.

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Fisher, Yuval, ed. Fractal Image Compression. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2472-3.

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Shukla, K. K., and M. V. Prasad. Lossy Image Compression. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2218-0.

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Barnsley, Michael. Fractal image compression. Wellesley, Mass: AK Peters, 1993.

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Barnsley, Michael. Fractal image compression. Wellesley, Mass: AK Peters, 1993.

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Storer, James A. Image and Text Compression. Boston, MA: Springer US, 1992.

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Storer, James A., ed. Image and Text Compression. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3596-6.

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S, Carasso Alfred, and National Institute of Standards and Technology (U.S.), eds. Image compression and deblurring. Gaithersburg, Md: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.

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Rabbani, Majid. Digital image compression techniques. Bellingham, Wash., USA: Spie Optical Engineering Press, 1991.

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Book chapters on the topic "Image compression"

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Salomon, David. "Image Compression." In Data Compression, 163–249. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_4.

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Salomon, David. "Image Compression." In Data Compression, 221–456. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8_5.

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Sha, Lei. "Image Compression." In Encyclopedia of GIS, 472–75. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_584.

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Walnut, David F. "Image Compression." In An Introduction to Wavelet Analysis, 371–95. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-1-4612-0001-7_12.

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Mann, Stephen. "Image Compression." In PACS, 257–80. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3651-9_10.

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Sha, Lei. "Image Compression." In Encyclopedia of GIS, 1–5. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_584-2.

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Salomon, David, and Giovanni Motta. "Image Compression." In Handbook of Data Compression, 443–730. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-903-9_7.

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Sundararajan, D. "Image Compression." In Digital Image Processing, 363–405. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6113-4_13.

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Salomon, David. "Image Compression." In A Guide to Data Compression Methods, 81–166. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21708-6_4.

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Weik, Martin H. "image compression." In Computer Science and Communications Dictionary, 750. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8640.

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Conference papers on the topic "Image compression"

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Li, Hui, Yan Lu, Masahiro Takei, Mitsuaki Ochi, Yoshifuru Saito, and Kiyoshi Horii. "Flow Image Compression Using Wavelets." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-1212.

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Abstract The wavelet compression technique was applied to turbulent image processing for reducing physical storage and extracting the compact dominant features in this study. The two compression methods, called zone and threshold compression, were employed. It was found that a high order wavelet basis provided good compression performance for compressing turbulent images and two compression methods exhibited almost same performance. It was realized that the compressed image had both lower compress ratio and larger correlation coefficients. By changing compression ratio the compressed images exhibited different scale structures in turbulent jet. This indicated clear that large-scale structure dominates the jet.
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Yi Yang, Oscar C. Au, Lu Fang, Xing Wen, and Weiran Tang. "Reweighted Compressive Sampling for image compression." In 2009 Picture Coding Symposium (PCS). IEEE, 2009. http://dx.doi.org/10.1109/pcs.2009.5167354.

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Deng, Chenwei, Weisi Lin, Bu-sung Lee, and Chiew Tong Lau. "Robust image compression based on compressive sensing." In 2010 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2010. http://dx.doi.org/10.1109/icme.2010.5583387.

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Hubbard-Featherstone, Casey J., Mark A. Garcia, and William Y. L. Lee. "Adaptive block compressive sensing for image compression." In 2017 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2017. http://dx.doi.org/10.1109/ivcnz.2017.8402490.

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McGuire, Michael D. "Is Fractal Image Compression Related to Cortical Image Compression?" In Applied Vision. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/av.1989.wb4.

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Fractals are customarily introduced with simple line replacement rules, for example the well known Koch curve. At each state of iteration single lines are replaced by combinations of lines according to a rule. Remarkable complexity can be built up this way as shown by five stages of this line replacement bush fractal1.
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Li, Hui, Masahiro Takei, Yoshifuru Saito, and Kiyoshi Horii. "Application of Wavelet Packet to Particle Image Velocimetry Technique." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2090.

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Abstract Our motive of this study is to develop an application of the wavelet packet to PIV image compression processing in order to improve the spatial resolution and reliability furthermore. It was found that the reconstructed PIV image with a lower compression ratio may emphasize particle edges at a relatively high spatial resolution, and the reconstructed PIV image with a higher compression ratio may display the large-scale motion of particles and may deduce noisy. In this study, the relative error of the wavelet packet image compression technique was lower than that of the standard wavelet image compression technique. The higher compression ratio of 64:1 can be realized without losing significant flow information in PIV processing. It could say that the wavelet packet could provide a better compression performance than the standard wavelet image compression technique when compressing PIV images.
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Maha Lakshmi, G. V. "Implementation of image compression using Fractal Image Compression and neural networks for MRI images." In 2016 International Conference on Information Science (ICIS). IEEE, 2016. http://dx.doi.org/10.1109/infosci.2016.7845301.

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Mailhes, Corinne, Paul Vermande, and Francis Castanie. "Spectral Image Compression." In 1989 Intl Congress on Optical Science and Engineering, edited by G. Duchossois, Frank L. Herr, and Rodolphe J. Zander. SPIE, 1989. http://dx.doi.org/10.1117/12.961492.

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Yang, Zhaohui, Yunhe Wang, Chang Xu, Peng Du, Chao Xu, Chunjing Xu, and Qi Tian. "Discernible Image Compression." In MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394171.3413968.

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Sabbatino, V. "Radar image compression." In Radar Systems (RADAR 97). IEE, 1997. http://dx.doi.org/10.1049/cp:19971771.

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Reports on the topic "Image compression"

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NETROLOGIC INC SAN DIEGO CA. Image Compression. Fort Belvoir, VA: Defense Technical Information Center, June 1990. http://dx.doi.org/10.21236/ada224242.

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Wang, Jun, and H. K. Huang. Digital Mammographic Image Compression. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada300271.

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Nakassis, Anastase, and Alfred Carasso. Image compression and deblurring. Gaithersburg, MD: National Institute of Standards and Technology, 2000. http://dx.doi.org/10.6028/nist.ir.6521.

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Boss, R. D., and E. W. Jacobs. Fractal-Based Image Compression. Fort Belvoir, VA: Defense Technical Information Center, September 1989. http://dx.doi.org/10.21236/ada215400.

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Jacobs, E. W., R. D. Boss, and Y. Fisher. Fractal-Based Image Compression, II. Fort Belvoir, VA: Defense Technical Information Center, June 1990. http://dx.doi.org/10.21236/ada226500.

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Reynolds, W. D. Jr. Image compression using the W-transform. Office of Scientific and Technical Information (OSTI), December 1995. http://dx.doi.org/10.2172/195703.

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Mazieres, Bertrand. A New Approach for Fingerprint Image Compression. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/763151.

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Hodges, Dewey H. AASERT-92/Image Compression & Wavelet Generation. Fort Belvoir, VA: Defense Technical Information Center, December 1996. http://dx.doi.org/10.21236/ada337454.

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Libert, John M. Guidance on Contactless Friction Ridge Image Compression. Gaithersburg, MD: National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.ir.8465.

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Libert, John M. Guidance on Contactless Friction Ridge Image Compression. Gaithersburg, MD: National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.sp.500-340.

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