Academic literature on the topic 'Predictive quantization'
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Journal articles on the topic "Predictive quantization"
Ngan, K. N., and H. C. Koh. "Predictive classified vector quantization." IEEE Transactions on Image Processing 1, no. 3 (July 1992): 269–80. http://dx.doi.org/10.1109/83.148602.
Full textDubnov, Shlomo. "Predictive Quantization and Symbolic Dynamics." Algorithms 15, no. 12 (December 19, 2022): 484. http://dx.doi.org/10.3390/a15120484.
Full textKlautau, A. B. R. "Predictive vector quantization with intrablock prediction support region." IEEE Transactions on Image Processing 8, no. 2 (1999): 293–95. http://dx.doi.org/10.1109/83.743862.
Full textHsueh-Ming Hang and J. Woods. "Predictive Vector Quantization of Images." IEEE Transactions on Communications 33, no. 11 (November 1985): 1208–19. http://dx.doi.org/10.1109/tcom.1985.1096238.
Full textLinden, J. "Channel optimized predictive vector quantization." IEEE Transactions on Speech and Audio Processing 8, no. 4 (July 2000): 370–84. http://dx.doi.org/10.1109/89.848219.
Full textRizvi, Syed A. "Entropy‐constrained predictive residual vector quantization." Optical Engineering 35, no. 1 (January 1, 1996): 187. http://dx.doi.org/10.1117/1.600889.
Full textMarcellin, M. W., T. R. Fischer, and J. D. Gibson. "Predictive trellis coded quantization of speech." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 1 (1990): 46–55. http://dx.doi.org/10.1109/29.45617.
Full textSchwarz, Stefan, and Markus Rupp. "Predictive Quantization on the Stiefel Manifold." IEEE Signal Processing Letters 22, no. 2 (February 2015): 234–38. http://dx.doi.org/10.1109/lsp.2014.2354258.
Full textWu, Yung-Gi. "Predictive classifier for image vector quantization." Optical Engineering 39, no. 9 (September 1, 2000): 2372. http://dx.doi.org/10.1117/1.1286465.
Full textRizvi, S. A., and N. M. Nasrabadi. "Predictive residual vector quantization [image coding]." IEEE Transactions on Image Processing 4, no. 11 (1995): 1482–95. http://dx.doi.org/10.1109/83.469930.
Full textDissertations / Theses on the topic "Predictive quantization"
Soong, Michael. "Predictive split vector quantization for speech coding." Thesis, McGill University, 1994. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=68054.
Full textSummation Product Codes (SPCs) are a family of structured vector quantizers that circumvent the complexity obstacle. The performance of SPC vector quantizers can be traded off against their storage and encoding complexity. Besides the complexity factors, the design algorithm can also affect the performance of the quantizer. The conventional generalized Lloyd's algorithm (GLA) generates sub-optimal codebooks. For particular SPC such as multistage VQ, the GLA is applied to design the stage codebooks stage-by-stage. Joint design algorithms on the other hand update all the stage codebooks simultaneously.
In this thesis, a general formulation and an algorithm solution to the joint codebook design problem is provided for the SPCs. The key to this algorithm is that every PC has a reference product codebook which minimizes the overall distortion. This joint design algorithm is tested with a novel SPC, namely "Predictive Split VQ (PSVQ)".
VQ of speech Line Spectral Frequencies (LSF's) using PSVQ is also presented. A result in this work is that PSVQ, designed using the joint codebook design algorithm requires only 20 bits/frame(20 ms) for transparent coding of a 10$ sp{ rm th}$ order LSF's parameters.
Abousleman, Glen Patrick. "Entropy-constrained predictive trellis coded quantization and compression of hyperspectral imagery." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186748.
Full textWang, Yan. "Predictive boundary point adaptation and vector quantization compression algorithms for CMOS image sensors /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?ECED%202007%20WANGY.
Full textRivera, Hernández Sergio. "Tensorial spacetime geometries carrying predictive, interpretable and quantizable matter dynamics." Phd thesis, Universität Potsdam, 2012. http://opus.kobv.de/ubp/volltexte/2012/6186/.
Full textWelche Tensorfelder G auf einer glatten Mannigfaltigkeit M können eine Raumzeit-Geometrie beschreiben? Im ersten Teil dieser Dissertation wird es gezeigt, dass nur stark eingeschränkte Klassen von Tensorfeldern eine Raumzeit-Geometrie darstellen können, nämlich Tensorfelder, die eine prädiktive, interpretierbare und quantisierbare Dynamik für Materiefelder ermöglichen. Die offensichtliche Abhängigkeit dieser Charakterisierung erlaubter tensorieller Raumzeiten von einer spezifischen Materiefelder-Dynamik ist keine Schwäche der Theorie, sondern ist letztlich genau das Prinzip, das die üblicherweise betrachteten Lorentzschen Mannigfaltigkeiten auszeichnet: diese stellen die metrische Geometrie dar, welche die Maxwellsche Elektrodynamik prädiktiv, interpretierbar und quantisierbar macht. Materiefeld-Dynamiken, welche die kausale Struktur von Maxwell-Elektrodynamik nicht respektieren, zwingen uns, eine andere Geometrie auszuwählen, auf der die Materiefelder-Dynamik aber immer noch prädiktiv, interpretierbar und quantisierbar sein muss. Diesen drei Voraussetzungen an die Materie entsprechen drei algebraische Voraussetzungen an das total symmetrische kontravariante Tensorfeld P, welches das Prinzipalpolynom der Materiefeldgleichungen (ausgedrückt durch das grundlegende Tensorfeld G) bestimmt: das Tensorfeld P muss hyperbolisch, zeitorientierbar und energie-differenzierend sein. Diese drei notwendigen Bedingungen an die Geometrie genügen, um alle aus der Lorentzschen Geometrie bekannten kinematischen Konstruktionen zu realisieren. Dies zeigen wir im ersten Teil der vorliegenden Arbeit unter Verwendung eines teilweise recht subtilen Wechselspiels zwischen konvexer Analysis, der Theorie partieller Differentialgleichungen und reeller algebraischer Geometrie. Im zweiten Teil dieser Dissertation erforschen wir allgemeine Eigenschaften aller solcher hyperbolischen, zeit-orientierbaren und energie-differenzierenden Geometrien. Physikalisch wichtig sind der Aufbau von frei fallenden und nicht rotierenden Laboratorien, das Auftreten modifizierter Energie-Impuls-Beziehungen und die Identifizierung eines Mechanismus, der erklärt, warum massive Teilchen, die sich schneller als einige masselosse Teilchen bewegen, Energie abstrahlen können, aber nur bis sie sich langsamer als alle masselossen Teilchen bewegen. Im dritten Teil der Dissertation ergründen wir die Quantisierung von Teilchen und Feldern auf tensoriellen Raumzeit-Geometrien, die die obigen physikalischen Bedingungen erfüllen. Eine wichtige Motivation dieser Untersuchung ist es, Techniken zur Berechnung der Zerfallsrate von Teilchen zu berechnen, die sich schneller als langsame masselose Teilchen bewegen. Wir finden, dass es wiederum die drei zuvor im klassischen Kontext identifizierten Voraussetzungen (der Hyperbolizität, Zeit-Orientierbarkeit und Energie-Differenzierbarkeit) sind, welche die Quantisierung allgemeiner linearer Elektrodynamik auf einer flächenmetrischen Raumzeit und die Quantizierung massiver Teilchen, die eine physikalische Energie-Impuls-Beziehung respektieren, erlauben. Wir erkunden auch systematisch, wie man Feldgleichungen aller Ableitungsordnungen generieren kann und beweisen einen Satz, der verallgemeinerte Dirac-Algebren bestimmt und die damit Reduzierung des Ableitungsgrades einer physikalischen Materiefeldgleichung ermöglicht. Der letzte Teil der vorliegenden Schrift skizziert ein bemerkenswertes Ergebnis, das mit den in dieser Dissertation dargestellten Techniken erzielt wurde. Insbesondere aufgrund der hier identifizierten dualen Abbildungen zwischen Teilchenimpulsen und -geschwindigkeiten auf allgemeinen tensoriellen Raumzeiten war es möglich zu zeigen, dass man die Gravitationsdynamik für hyperbolische, zeit-orientierbare und energie-differenzierende Geometrien nicht postulieren muss, sondern dass sich das Problem ihrer Konstruktion auf eine rein mathematische Aufgabe reduziert: die Lösung eines homogenen linearen Differentialgleichungssystems. Dieses weitreichende Ergebnis über modifizierte Gravitationstheorien ist eine direkte (aber schwer herzuleitende) Folgerung der Forschungsergebnisse dieser Dissertation. Die abstrakte Theorie dieser Doktorarbeit wird durch mehrere instruktive Beispiele illustriert.
Horvath, Matthew Steven. "Performance Prediction of Quantization Based Automatic Target Recognition Algorithms." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1452086412.
Full textHuang, Bihong. "Second-order prediction and residue vector quantization for video compression." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S026/document.
Full textVideo compression has become a mandatory step in a wide range of digital video applications. Since the development of the block-based hybrid coding approach in the H.261/MPEG-2 standard, new coding standard was ratified every ten years and each new standard achieved approximately 50% bit rate reduction compared to its predecessor without sacrificing the picture quality. However, due to the ever-increasing bit rate required for the transmission of HD and Beyond-HD formats within a limited bandwidth, there is always a requirement to develop new video compression technologies which provide higher coding efficiency than the current HEVC video coding standard. In this thesis, we proposed three approaches to improve the intra coding efficiency of the HEVC standard by exploiting the correlation of intra prediction residue. A first approach based on the use of previously decoded residue shows that even though gains are theoretically possible, the extra cost of signaling could negate the benefit of residual prediction. A second approach based on Mode Dependent Vector Quantization (MDVQ) prior to the conventional transformed scalar quantization step provides significant coding gains. We show that this approach is realistic because the dictionaries are independent of QP and of a reasonable size. Finally, a third approach is developed to modify dictionaries gradually to adapt to the intra prediction residue. A substantial gain is provided by the adaptivity, especially when the video content is atypical, without increasing the decoding complexity. In the end we get a compromise of complexity and gain for a submission in standardization
Vasconcelos, Nuno Miguel Borges de Pinho Cruz de. "Library-based image coding using vector quantization of the prediction space." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/62918.
Full textIncludes bibliographical references (leaves 122-126).
by Nuno Miguel Borges de Pinho Cruz de Vasconcelos.
M.S.
Boland, Simon Daniel. "High quality audio coding using the wavelet transform." Thesis, Queensland University of Technology, 1998.
Find full textClayton, Arnshea. "The Relative Importance of Input Encoding and Learning Methodology on Protein Secondary Structure Prediction." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_theses/19.
Full textKamath, Vidya P. "Enhancing Gene Expression Signatures in Cancer Prediction Models: Understanding and Managing Classification Complexity." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3653.
Full textBooks on the topic "Predictive quantization"
So, Kevin Kin Man. A new quantization technique for linear predictive speech coding. Manchester: University of Manchester, 1994.
Find full textRobust Source Coding of Images With Predictive Trellis - Coded Quantization. Storming Media, 1996.
Find full textConditional entropy-constrained residual VQ with application to image coding. [Washington, DC: National Aeronautics and Space Administration, 1996.
Find full textC, Chung Wilson, Smith Mark J. T, and United States. National Aeronautics and Space Administration., eds. Conditional entropy-constrained residual VQ with application to image coding. [Washington, DC: National Aeronautics and Space Administration, 1996.
Find full textC, Chung Wilson, Smith Mark J. T, and United States. National Aeronautics and Space Administration., eds. Conditional entropy-constrained residual VQ with application to image coding. [Washington, DC: National Aeronautics and Space Administration, 1996.
Find full textC, Chung Wilson, Smith Mark J. T, and United States. National Aeronautics and Space Administration., eds. Conditional entropy-constrained residual VQ with application to image coding. [Washington, DC: National Aeronautics and Space Administration, 1996.
Find full textBook chapters on the topic "Predictive quantization"
Gersho, Allen, and Robert M. Gray. "Predictive Quantization." In Vector Quantization and Signal Compression, 203–23. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3626-0_7.
Full textGersho, Allen, and Robert M. Gray. "Predictive Vector Quantization." In Vector Quantization and Signal Compression, 487–517. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3626-0_13.
Full textSun, Zhen, Yue-Nan Li, and Zhe-Ming Lu. "Side-Match Predictive Vector Quantization." In Lecture Notes in Computer Science, 405–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553939_58.
Full textGoodwin, Graham C., Jan Østergaard, Daniel E. Quevedo, and Arie Feuer. "A Vector Quantization Approach to Scenario Generation for Stochastic NMPC." In Nonlinear Model Predictive Control, 235–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_19.
Full textBhaskar, Udaya. "Adaptive Predictive Coding with Transform Domain Quantization." In Speech and Audio Coding for Wireless and Network Applications, 265–69. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3232-3_34.
Full textAndersen, Søren Vang, Søren Holdt Jensen, and Egon Hansen. "Vector-Predictive Speech Coding with Quantization Noise Modelling." In Signal Analysis and Prediction, 429–42. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_30.
Full textYakut, Mehmet. "Multiscale Image Representation Using Switched Codebook Predictive Vector Quantization." In Proceedings of The 17th International Symposium on Computer and Information Sciences, 86–90. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9780429332821-20.
Full textShi, Min, and Shengli Xie. "A New Predictive Vector Quantization Method Using a Smaller Codebook." In Lecture Notes in Computer Science, 229–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539087_28.
Full textHirose, Akira, and Tomoyuki Nagashima. "Predictive Self-Organizing Map for Vector Quantization of Migratory Signals." In Artificial Neural Networks — ICANN 2002, 884–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_143.
Full textGersho, Allen, and Robert M. Gray. "Linear Prediction." In Vector Quantization and Signal Compression, 83–129. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3626-0_4.
Full textConference papers on the topic "Predictive quantization"
Rizvi, Syed A., Lin-Cheng Wang, and Nasser M. Nasrabadi. "Entropy-constrained predictive residual vector quantization." In Photonics East '95, edited by Raghuveer M. Rao, Soheil A. Dianat, Steven W. McLaughlin, and Martin Hassner. SPIE, 1995. http://dx.doi.org/10.1117/12.228223.
Full textRizvi, Syed A., Nasser M. Nasrabadi, and Lin-Cheng Wang. "Variable-rate predictive residual vector quantization." In Visual Communications and Image Processing '95, edited by Lance T. Wu. SPIE, 1995. http://dx.doi.org/10.1117/12.206758.
Full textHashemi, Mahmoud R., Tet H. Yeap, and Sethuraman Panchanathan. "Predictive vector quantization using neural networks." In Electronic Imaging '97, edited by Nasser M. Nasrabadi and Aggelos K. Katsaggelos. SPIE, 1997. http://dx.doi.org/10.1117/12.269776.
Full textNarayan, Ajai, and Tenkasi V. Ramabadran. "Image coding through predictive vector quantization." In San Diego '92, edited by Andrew G. Tescher. SPIE, 1993. http://dx.doi.org/10.1117/12.139093.
Full textDesappan, Kumar, Mihir Mody, Manu Mathew, Pramod Swami, and Praveen Eppa. "CNN Inference: Dynamic and Predictive Quantization." In 2018 IEEE 8th International Conference on Consumer Electronics - Berlin. IEEE, 2018. http://dx.doi.org/10.1109/icce-berlin.2018.8576251.
Full textChen, Yuepeng, Jingxin Zhang, Shenpeng Li, and Li Chai. "Robust H∞ optimal signal predictive quantization." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400725.
Full textKyeong Ho Yang, Wenwu Zhu, and A. F. Faryar. "Perceptual quantization for predictive coding of images." In Proceedings of 6th International Conference on Image Processing (ICIP'99). IEEE, 1999. http://dx.doi.org/10.1109/icip.1999.822922.
Full textMohsenian, N., and N. M. Nasrabadi. "Predictive vector quantization using a neural network." In Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319793.
Full textMohsenian, Nader, and Nasser M. Nasrabadi. "Neural net approach to predictive vector quantization." In Applications in Optical Science and Engineering, edited by Petros Maragos. SPIE, 1992. http://dx.doi.org/10.1117/12.131465.
Full textGollin, Nicola, Michele Martone, Michelangelo Villano, Paola Rizzoli, and Gerhard Krieger. "Predictive Quantization for Staggered Synthetic Aperture Radar." In 2019 12th German Microwave Conference (GeMiC). IEEE, 2019. http://dx.doi.org/10.23919/gemic.2019.8698197.
Full textReports on the topic "Predictive quantization"
Marvel, Lisa M. Robust Source Coding of Images With Predictive Trellis - Coded Quantization. Fort Belvoir, VA: Defense Technical Information Center, September 1996. http://dx.doi.org/10.21236/ada315312.
Full text