The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

LOCO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with an extended family of Golomb-type codes, which are adaptively chosen, and an embedded alphabet extension for coding of low-entropy image regions. LOCO-I attains compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level. We discuss the principles underlying the design of LOCO-I, and its standardization into JPEC-LS.

[1]  Meir Feder,et al.  A universal finite memory source , 1995, IEEE Trans. Inf. Theory.

[2]  R. Schafer,et al.  Two-dimensional linear prediction and its application to adaptive predictive coding of images , 1984 .

[3]  Bernd Meyer,et al.  TMW - a new method for lossless image compression , 1997 .

[4]  Gadiel Seroussi,et al.  On adaptive strategies for an extended family of Golomb-type codes , 1997, Proceedings DCC '97. Data Compression Conference.

[5]  David A. Clunie,et al.  Lossless compression of grayscale medical images: effectiveness of traditional and state-of-the-art approaches , 2000, Medical Imaging.

[6]  Donald E. Knuth,et al.  Dynamic Huffman Coding , 1985, J. Algorithms.

[7]  Abraham Lempel,et al.  A universal algorithm for sequential data compression , 1977, IEEE Trans. Inf. Theory.

[8]  Jorma Rissanen,et al.  Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.

[9]  Nasir D. Memon,et al.  Lossless interframe image compression via context modeling , 1998, Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225).

[10]  Erik Ordentlich,et al.  A low-complexity modeling approach for embedded coding of wavelet coefficients , 1998, Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225).

[11]  David A. Huffman,et al.  A method for the construction of minimum-redundancy codes , 1952, Proceedings of the IRE.

[12]  A.N. Netravali,et al.  Picture coding: A review , 1980, Proceedings of the IEEE.

[13]  Shuo-Yen Robert Li Fast Constant Division Routines , 1985, IEEE Trans. Computers.

[14]  Glen G. Langdon,et al.  On the JPEG model for lossless image compression , 1992, Data Compression Conference, 1992..

[15]  Jorma Rissanen,et al.  Universal coding, information, prediction, and estimation , 1984, IEEE Trans. Inf. Theory.

[16]  Jorma Rissanen,et al.  Applications of universal context modeling to lossless compression of gray-scale images , 1995, Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers.

[17]  Neri Merhav,et al.  Coding of sources with two-sided geometric distributions and unknown parameters , 2000, IEEE Trans. Inf. Theory.

[18]  P.G. Howard,et al.  Fast and efficient lossless image compression , 1993, [Proceedings] DCC `93: Data Compression Conference.

[19]  T. K. Truong,et al.  Comparison of international standards for lossless still image compression , 1994, Proc. IEEE.

[20]  Glen G. Langdon,et al.  Experiments in lossless and virtually lossless image-compression algorithms , 1995, Electronic Imaging.

[21]  Guillermo Sapiro,et al.  LOCO-I: a low complexity, context-based, lossless image compression algorithm , 1996, Proceedings of Data Compression Conference - DCC '96.

[22]  Solomon W. Golomb Sources Which Maximize the Choice of a Huffman Coding Tree , 1980, Inf. Control..

[23]  Nasir D. Memon,et al.  On ordering color maps for lossless predictive coding , 1996, IEEE Trans. Image Process..

[24]  Neri Merhav,et al.  Optimal prefix codes for sources with two-sided geometric distributions , 2000, IEEE Trans. Inf. Theory.

[25]  Gadiel Seroussi,et al.  Sequential prediction and ranking in universal context modeling and data compression , 1997, IEEE Trans. Inf. Theory.

[26]  Meir Feder,et al.  SICLIC: a simple inter-color lossless image coder , 1999, Proceedings DCC'99 Data Compression Conference (Cat. No. PR00096).

[27]  Jorma Rissanen,et al.  Generalized Kraft Inequality and Arithmetic Coding , 1976, IBM J. Res. Dev..

[28]  Neri Merhav,et al.  Modeling and low-complexity adaptive coding for image prediction residuals , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[29]  Nasir D. Memon,et al.  Context-based, adaptive, lossless image coding , 1997, IEEE Trans. Commun..

[30]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[31]  Neri Merhav,et al.  Some properties of sequential predictors for binary Markov sources , 1993, IEEE Trans. Inf. Theory.

[32]  Robert F. Rice,et al.  Some practical universal noiseless coding techniques , 1979 .

[33]  Xiaolin Wu,et al.  Lossless compression of continuous-tone images via context selection, quantization, and modeling , 1997, IEEE Trans. Image Process..

[34]  Glen G. Langdon,et al.  Centering of context-dependent components of prediction-error distributions of images , 1993, Optics & Photonics.

[35]  Paul G. Howard,et al.  Fast and Eecient Lossless Image Compression Fast and Efficient Lossless Image Compression , 1993 .

[36]  Glen G. Langdon,et al.  Universal modeling and coding , 1981, IEEE Trans. Inf. Theory.

[37]  Solomon W. Golomb,et al.  Run-length encodings (Corresp.) , 1966, IEEE Trans. Inf. Theory.

[38]  Neri Merhav,et al.  Relations between entropy and error probability , 1994, IEEE Trans. Inf. Theory.

[39]  R. F. Rice,et al.  Some practical universal noiseless coding techniques, part 2 , 1983 .

[40]  Bede Liu,et al.  A novel approach for coding color quantized images , 1993, IEEE Trans. Image Process..

[41]  Jukka Teuhola,et al.  A Compression Method for Clustered Bit-Vectors , 1978, Inf. Process. Lett..

[42]  Stephen Todd,et al.  Parameter Reduction and Context Selection for Compression of Gray-Scale Images , 1985, IBM J. Res. Dev..

[43]  S. A. Martucci,et al.  Reversible compression of HDTV images using median adaptive prediction and arithmetic coding , 1990, IEEE International Symposium on Circuits and Systems.

[44]  David C. van Voorhis,et al.  Optimal source codes for geometrically distributed integer alphabets (Corresp.) , 1975, IEEE Trans. Inf. Theory.

[45]  S. Golomb Run-length encodings. , 1966 .