FAQ:What is the state of the art in lossless image compression?
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JBIG (bi-level, arithmetic coder):
The JBIG algorithm is one of the best available for lossless image compression. For an introduction to JBIG, see FAQ:Introduction to JBIG.
JBIG works best on bi-level images (like faxes) and also works well on gray-coded grey scale images up to about six or so bits per pixel. You just apply JBIG to the bit planes individually. For more bits/pixel, lossless JPEG provides better performance, sometimes. (For JPEG, see FAQ:Introduction to JPEG.)
You can find the specification of JBIG in International Standard ISO/IEC 11544 or in ITU-T Recommendation T.82. You can order a copy directly from ISO or ITU or from your National Standards Body. In the USA, call ANSI at (212) 642-4900.
See also the MG system containing an implementation of the 'FELICS' algorithm of P.G. Howard and J.S. Vitter. FELICS usually gives better and faster compression than lossless JPEG, at least for 8-bit greyscale images (see FAQ:Where can I get image compression programs?). From the MG README file:
The MG system is a suite of programs for compressing and indexing text and images. Most of the functionality implemented in the suite is as described in the book Managing Gigabytes.
These features include:
- text compression using a Huffman-coded semi-static word-based scheme
- two-level context-based compression of bi-level images
- FELICS lossless compression of gray-scale images
- combined lossy/lossless compression for textual images
- indexing algorithms for large volumes of text in limited main memory
- index compression
- a retrieval system that processes boolean and ranked queries
- an X windows interface to the retrieval system
Also Paul Howard's PhD thesis, which among other things describes FELICS.
JPEG-LS (greyscale + colorspace, Golomb-Rice)
JPEG-LS is being developed by ISO/IEC JTC1/SC29/WG1 (final committee draft document FCD14495-1 as of July 1997), and it is based on HP's LOCO-I algorithm.
The main feature of JPEG-LS is its superior placement in the compression/complexity trade-off curve. Tested over a wide variety of image types, it was shown to be, on the average, within about 4% of the best available lossless image compression at a fraction of the complexity. In particular, JPEG-LS significantly outperforms FELICS and lossless JPEG Huffman at similar levels of complexity (it also outperforms lossless JPEG arithmetic, which is of significantly higher complexity). [...]
A software implementation of JPEG-LS, is now available at http://www.hpl.hp.com/loco/ There, the DCC'96 paper on LOCO-I is also available. The standard draft is also available through a link to the official JPEG Web site.
CALIC (greyscale, Huffman + arithmetic coder)
BTPC, APT and PyramidWorkshop (greyscale + colorspace, Huffman + arithmetic coder)
BMF (greyscale + colorspace)
Glicbawls, TMW and TMWLego (greyscale + colorspace, arithmetic coder)
MRP - Adaptive Context Quantization (greyscale, Huffman + arithmetic coder)
PCIF (colorspace, Huffman)
- high entropy
- high level of detail
- high resolution
|Compressor||Bit per 1bit pixel|
|GIF (libGIF v4.1.4)||0.1255|
|PNG (libPNG v1.2.8)||0.0772|
|JBIG (libJBIG v1.6)||0.0294|
|BMF v2.0ß (internal)||0.0256|
|FELICS (Managing GigaBytes v1.2)||0.0251|
|Compressor||Below 8bit entropy||Bit per 8bit pixel|
|GIF (libGIF v4.1.4)||0.753||6.298|
|Lossless S+P v4.04||1.882||5.169|
|JPEG (IJG v6.2 + lossless + arithmetic)||3.317||3.734|
|PNG (libPNG v1.2.8)||3.560||3.491|
|FELICS (Managing GigaBytes v1.2)||3.623||3.428|
|JPEG-2000 (Kakadu v5.1)||3.992||3.059|
|JPEG-2000 (OpenJPEG v1.0)||3.992||3.058|
|LOCO-I (Columbia University)||4.044||3.007|
|BMF v2.0ß (internal)||4.292||2.759|
|Compressor||Below 8bit entropy||Bit per 24bit pixel|
|BMF v2.0ß (internal)||3.89||11.63|
|PNG (libPNG v1.2.8)||3.92||11.54|
|LOCO-I (Columbia University)||4.42||10.04|
|JPEG-2000 (OpenJPEG v1.0)||4.51||9.78|
|JPEG-2000 (Kakadu v5.1)||4.51||9.78|
- ↑ "Managing Gigabytes: Compressing and Indexing Documents and Images", I.H. Witten, A. Moffat, and T.C. Bell; Van Nostrand Reinhold, New York, 1994, ISBN 0-442-01863-0; US $54.95; call 1 (800) 544-0550 to order
- ↑ "The Design and Analysis of Efficient Lossless Data Compression Systems", Paul Glor Howard, June 1993
- ↑ "LOCO-I: A Low Complexity, Context-Based, Lossless Image Compression Algorithm", M. Weinberger, G. Seroussi, G. Sapiro - Proc. IEEE Data Compression Conference, Snowbird, Utah, March-April 1996
- ↑ Wu, Xiaolin and Memon, Nasir,"Context-Based, Adaptive, Lossless Image Coding", IEEE Transactions on Communications, Vol. 45, No. 4, April 1997.
- ↑ "Efficient General-Purpose Image Compression with Binary Tree Predictive Coding", J. A. Robinson, IEEE Transactions on Image Processing, Vol 6, No 4, April 1997, pp 601-607.
- ↑ "Adaptive Prediction Trees for Image Compression", J. A. Robinson, IEEE Transactions on Image Processing, Vol 15, No 5, August 2006, pp 2131-2145.
- ↑ Glicbawls - Grey Level Image Compression By Adaptive Weighted Least Squares, Bernd Meyer & Peter Tischer, School of Computer Science and Software Engineering, Monash University
- ↑ TMW - a New Method for Lossless Image Compression, Bernd Meyer & Peter Tischer, Department of Computer Science, Monash University
- ↑ TMWLego - An Object Oriented Image Modelling Framework, Bernd Meyer & Peter E. Tischer, Data Compression Conference 2001: 504
- ↑ "Lossless Coding of Still Images Using Minimum-Rate Predictors", Ichiro Matsuda, Hirofumi Mori and Susumu Itoh, Proceedings of 2000 IEEE International Conference on Image Processing (ICIP 2000), Vol.I, pp.132-135, Sep. 2000.
- ↑ The Polyomino Compressed Image Format, Stefano Brocchi
- ↑ "GraLIC - new lossless image compressor", Alexander Ratushnyak