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Wykorzystanie adaptacyjnego kodera arytmetycznego z przełączaniem kontekstów do bezstratnej kompresji obrazów

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Warianty tytułu
EN
Application of a context-switching adaptive arithmetic encoder to lossless image compression
Języki publikacji
PL
Abstrakty
PL
W artykule zaprezentowano metodę bezstratnej kompresji obrazów. Przedstawiono klasyfikację współczesnych metod, a następnie scharakteryzowano podstawowe typy predykcyjnego kodowania. Zaproponowano efektywną metodę mieszania predyktorów wraz z wykorzystaniem kontekstowej korekcji błędu, jako efektywny sposób wstępnego modelowania obrazów. Opracowano też wielokontekstowy, adaptacyjny koder arytmetyczny, który poddano szczegółowej analizie. Połączenie obu propozycji zaowocowało otrzymaniem wydajnej i szybkiej metody kompresji obrazów, którą można w łatwy sposób zaimplementować zarówno sprzętowo, jak i programowo.
EN
A method of lossless image compression is described in this paper. A classification of contemporary methods is provided, and basic types of predictive coding are characterized. An effective method of blending predictors together with an application of a context error correction is proposed as an effective way for a first-stage image modeling. The developed, multi-context, adaptive arithmetic encoder is analyzed in details. The combination of the two proposals resulted in obtaining an effective and fast image compression method, which is easily implementable in both software and hardware.
Wydawca
Rocznik
Strony
101--118
Opis fizyczny
Bibliogr. 31 poz., rys., wykr., tab.
Twórcy
autor
  • Instytut Architektury Komputerów i Telekomunikacji, Wydział Informatyki,Politechnika Szczecińska
Bibliografia
  • [1] Avcibas L, Memon N., Sankur B., Sayood K., A progressive Lossless/Near-Lossless image compression algorithm. IEEE Signal Processing Letters, 2002, vol. 9, no. 10, s. 312-314 (dokument w wersji rozszerzonej).
  • [2] Daaboul A., Local prediction for lossless image compression. Proceedings of the Prague Stringology Club Workshop '98, s. 44-50.
  • [3] Deng G., Ye H., A general framework for the second-level adaptive prediction. Proceedings of ICASSP '03, April 2003, vol. 3, s. III_237-240.
  • [4] Deng G., Ye H., Lossless image compression using adaptive predictor combination, symbol map-ping and context filtering. Proceedings of IEEE 1999 International Conference on Image Processing, Kobe, Japan, Oct. 1999, vol. 4, s. 63-67.
  • [5] Dziurzański R, Mąka T., Ulacha G., Stasiński R., A lossless compression system realization utilizing phit-serial network-on-chip paradigm. Proceedings of 13th European Signal Processing Conference EUSIPCO-07 CD, September 2007.
  • [6] Gallager R. G., Variations on a theme by Huffman. IEEE Transactions on Information Theory, November 1978, vol. 24, no. 6, s. 668-674.
  • [7] Golchin F., Paliwal K. K., A lossless image coder with context classification, adaptive prediction and adaptive entropy coding. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Seattle, Washington, USA, May 1998, s. 2545-2548.
  • [8] Jiang J., Grecos C, Towards an improvement on prediction accuracy in JPEG-LS. Optical Engineering, SPIE, 2002, vol. 41, no. 2, s. 335-341.
  • [9] Lih-Jen Kau, Yuan-Pei Lin, Lossless image coding using a switching predictor with run-length encodings. Proceedings of IEEE International Conference on Multimedia and Expo, June 2004, vol. 2, s. 1155-1158.
  • [10] Kuroki Y, Ueshige Y, Ohta T., An estimation of the predictors implemented by shift operation, addition, and/or substraction. Proceedings of International Conference on Image Processing 2001, s. 474-477.
  • [11] Marcellin M., Gormish M., Bilgin A., Boliek M., An Overview of JPEG-2000. Proceedings Data Compression Conference, Snowbird, Utah, March 2000, s. 523-541.
  • [12] Marusic S., Deng G., A study of two new adaptive predictors for lossless imagecompression. Proceedings of IEEE 1997 International Conference on Image Processing, Oct. 1997, vol. 2, s. 286-289.
  • [13] Marusic S., Deng G., New prediction schemes for lossless coding of fullband and subband images. Signal Processing: Image Communication, 1999, vol. 14, s. 869-878.
  • [14] Matsuda I., Ozaki N., Umezu Y., Itoh S., Lossless coding using Variable Blok-Size adaptive prediction optimized for each image. Proceedings of 13th European Signal Processing Conference EUSIPCO-05 CD, September 2005.
  • [15] Meyer B., Tischer R, Glicbawls - Grey Level Image Compression by Adaptive Weighted Least Squares. Proceedings of Data Compression Conference 2001, s. 503.
  • [16] Meyer B., Tischer P., TMW - a new method for lossless image compression. Proceedings of International Picture Coding Symposium (PCS97), Berlin, Germany, September 1997, s. 533-538.
  • [17] Meyer B., Tischer R, TMW(Lego) - An Object Oriented Image Modelling Framework. Proceedings of Data Compression Conference 2001, s. 504.
  • [18] Park S.-G., Delp E. J., Adaptive lossless video compression using an integer wavelet transform. Proceedings of International Conference on Image Processing ICIP '04, 24-27 October 2004, vol. 4, s. 2251-2254.
  • [19] Sayood K., Introduction to Data Compression. 2nd ed., Morgan Kaufmann Publ., 2002.
  • [20] Seemann T., Tisher P., Generalized locally adaptive DPCM. Department of Computer Science Technical Report CS97/301, Monash University, Australia, s. 1-15.
  • [21] Seemann T., Tischer P., Meyer B., History-Based Blending of Image Sub-Predictors. Proceedings of Picture Coding. Symposium, Berlin, Germany, 1997, s. 147-151.
  • [22] Strutz T., Context-Based Adaptive Linear Prediction for Lossless Image Coding. 4th International ITG Conference on Source and Channel Coding, Berlin, Germany, 28-30 January, 2002, s. 105-109.
  • [23] Ulacha G., Stasinski R., Dziurzanski R, Olejnik R., Lossless compression system architecture dedicated to Networks on Chips. Proceedings of Int. Conf. on Signals and Electronic Systems (ICSES'06), Łódź, Poland 2006, s. 235-238.
  • [24] Ulacha G., Stasinski R., On context-based predictive techniques for lossless image compression. Proceedings of 12th International Workshop on Systems, Signals & Image Processing - IWSSIP 2005, Chalkida, Greece 2005, s. 345-348.
  • [25] Ulacha G., Stasinski R., Parameter choice for predictor blending and application in lossless image coding. Pomiary, automatyka, kontrola, 2006, no. 7bis, s. 95-97.
  • [26] Wang H., Zhang D., A linear model and its application in lossless image coding. Signal Processing: Image Communication, 2004, vol. 19, s. 955-958.
  • [27] Weinberger M. J., Seroussi G., Sapiro G., LOCO-I: Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS. IEEE Trans, on Image Processing, vol. 9, No. 8, August 2000, s. 1309-1324.
  • [28] Wu X., Memon N. D., CALIC - A Context Based Adaptive Lossless Image Coding Scheme. IEEE Trans, on Communications, May 1996, vol. 45, s. 437^444.
  • [29] Ye H., A study on lossless compression of greyscale images. Department of Electronic Engineering, La Trobe University, October 2002 (PhD Thesis).
  • [30] Ye H., Deng G., Devlin J. C, Adaptive linear prediction for lossless coding of greyscale images. Proc. IEEE Int. Conf. on Image Processing (CDROM), Vancouver, Canada, September 2000.
  • [31] Ye H., Deng G., Devlin J. C, A weighted least squares method for adaptive prediction in lossless image compression. Proc. Picture Coding Symposium, Saint-Malo, France, 20
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-AGH1-0016-0081
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