Zastosowanie adaptacyjnej predykcji liniowej z przełączaniem kontekstów do bezstratnej kompresji obrazów

• Autorzy: Ulacha G.
• Języki publikacji: PL

Abstrakty

PL

W artykule omówiono potrzebę stosowania bezstratnej kompresji obrazów Zaprezentowano analizę efektywności szybkich metod adaptacji współczynników predykcji. Przedstawiono mieszany model M-LMS jako efektywny sposób modelowania, który w połączeniu z adaptacyjnym koderem arytmetycznym pozwala uzyskiwać wysoki stopień kompresji obrazów. Otrzymano rezultaty zbliżone do metody GLICBAWLS przy siedmiokrotnie krótszym czasie kodowania. Praca finansowana ze środków budżetowych na naukę w ramach grantu przyznanego na lata 2007-2010.

EN

In this paper, we present an analysis of adaptive linear prediction, focusing on difficulties in algorithm designing for two-dimensional signals. We discuss the basie LMS algorithm an its extensions. A novel approach for context switching is described which, in composition with adaptive method blending, allows us to obtain high efficiency ofthe proposed M-LMS method with low implementation complexity. The experimental results show that M-LMS maintains the same efficiency that the GLlCBAWLS method, known from literature. but is characterized with seven times shorter encoding time.

Słowa kluczowe

PL

bezstratna kompresja obrazu; kodowanie kontekstowe; skumulowany błąd predykcji; koder arytmetyczny

EN

lossless image compression; context coding; cumulated predictor error; arithmetic coder

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Metody Informatyki Stosowanej
• Rocznik: 2009
• Tom: nr 1 (18)
• Strony: 125--137
• Opis fizyczny: Bibliogr. 25 poz., tab.

Twórcy

autor

Ulacha G.

• Zachodniopomorski Uniwersytet Technologiczny w Szczecinie, Wydział Informatyki

Bibliografia

• [1] Sayood K., Introduction to Data Compression, 2nd edition, Morgan Kaufmann Publ., 2002.
• [2] Xiang Xie, GuoLin Li, ZhiHua Wang, A Near-Lossless Image Compression Algorithm Suitable for Hardware Design in Wireless Endoscopy System, EURASIP Journal on Advances in Signal Processing, vol. 2007, Article ID 82160, 13 pages.http://www.hindawi.com/Getpdf.aspx?doi=10.1155/2007/82160
• [3] Chen X. et al., Lossless Compression for Space Imagery in a Dynamically Reconfigurable Architecture, In Proc. of International Workshop on Applied Reconfigurable Computing (ARC2008), LNCS 4943, s.336-341, March 2008.
• [4] Lossless Data Compression. Recommendation for Space Data System Standards, CCSDS 120.1-G-1. Green Book. Issue 1. Washington, D.C.: CCSDS, June 2007.
• [5] 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.
• [6] 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.
• [7] 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.
• [8] Meyer B., Tischer P., TMWLego - An Object Oriented Image Modelling Framework, Proceedings of Data Compression Conference 2001, s. 504.
• [9] Ye H., A study on lossless compression of greyscale images, PhD thesis, Department of Electronic Engineering, La Trobe University, October 2002
• [10] 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, Antalya - Turkey, 4-8 September 2005.
• [11] G. Ulacha, R. Stasiński, A New Context-based Lossless Image Coding Method, Proceedings of 13th International Conference on Systems, Signals and Image Processing IWSSIP 2006, Budapest, Hungary 2006, pp. 215-218.
• [12] G. Ulacha, R. Stasiński, On context-based predictive techniques for lossless image compression, Proceedings of 12th International Workshop on Systems, Signals & Image Processing - IWSSIP 2005, Chalkida, Greece 2005, pp. 345-348.
• [13] M. J. Weinberger, G. Seroussi, G. Sapiro, LOCO-I: A low complexity, context-based, lossless image compression algorithm. Proc. of DCC'96, March-April 1996, Snowbird, Utah, pp. 140-149.
• [14] X. Wu, N. D. Memon, CALIC – A Context Based Adaptive Lossless Image Coding Scheme, IEEE Trans. on Communications, May 1996, vol. 45, pp. 437-444.
• [15] F. Golchin, K. K. Paliwal, 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, pp. 2545-2548.
• [16] G. Ulacha, R. Stasiński, New simple context-based predictive technique for lossless image compression, Proceedings of 13th European Signal Processing Conference EUSIPCO-07 CD, Poznań, Poland, September 2007, pp. 990-993.
• [17] G. Deng, Adaptive predictor combination for lossless image coding, Proceedings of 10th European Conference on Signal Processing EUSIPCO, Tampere, Finland, Sept. 2000, pp. 1141-1144.
• [18] 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.
• [19] Wang H., Zhang D., A linear model and its application in lossless image coding, Signal Processing: Image Communication, 2004, vol. 19, s. 955-958.
• [20] Marusic S., Deng G., Adaptive prediction for lossless image compression, Signal Processing: Image Communications, May 2002, vol. 17, s. 363-372.
• [21] G. Ulacha, R. Stasiński, A Time-Effective Lossless Coder Based on Hierarchical Contexts and Adaptive Predictors, Proceedings of 14th IEEE Mediterranean Electrotechnical Conference MELECON’08, Ajaccio, France, 5-7 May 2008, pp. 829-834.
• [22] Przelaskowski A., Kompresja danych: podstawy, metody bezstratne, kodery obrazów, Warszawa, Wydawnictwo BTC 2005.
• [23] G. Ulacha, R. Stasiński, A New Simple Context Lossless Image Coding Algorithm Based on Adaptive Context Arithmetic Coder, Proceedings of 15th International Conference on Systems, Signals and Image Processing IWSSIP 2008, Bratislava, Slovak Republic, 25-28 June 2008, pp. 45-48.
• [24] Meyer B., Tischer P., Glicbawls - Grey Level Image Compression by Adaptive Weighted Least Squares, Proceedings of Data Compression Conference 2001, s. 503.
• [25] Ye H., Deng G., Devlin J. C., Adaptive linear prediction for lossless coding of greyscale images, in Proc. IEEE Int. Conf. on Image Processing (CDROM), Vancouver, Canada, September 2000.