Lossless compression of continuous-tone images via context selection, quantization, and modeling
- 1 May 1997
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 6 (5) , 656-664
- https://doi.org/10.1109/83.568923
Abstract
Context modeling is an extensively studied paradigm for lossless compression of continuous-tone images. However, without careful algorithm design, high-order Markovian modeling of continuous-tone images is too expensive in both computational time and space to be practical, Furthermore, the exponential growth of the number of modeling states in the order of a Markov model can quickly lead to the problem of context dilution; that is, an image may not have enough samples for good estimates of conditional probabilities associated,vith the modeling states, In this paper, new techniques for context modeling of DPCM errors are introduced that can exploit context-dependent DPCM error structures to the benefit of compression. New algorithmic techniques of forming and quantizing modeling contexts are also developed to alleviate the problem of context dilution and reduce both time and space complexities. By innovative formation, quantization, and use of modeling contexts, the proposed lossless image coder has highly competitive compression performance and yet remains practical.Keywords
This publication has 15 references indexed in Scilit:
- Fast and efficient lossless image compressionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Lossless image compression with lossy image using adaptive prediction and arithmetic codingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Vector quantization of contextual information for lossless image compressionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Prediction trees and lossless image compressionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Applications of universal context modeling to lossless compression of gray-scale imagesIEEE Transactions on Image Processing, 1996
- Centering of context-dependent components of prediction-error distributions of imagesPublished by SPIE-Intl Soc Optical Eng ,1993
- New methods for lossless image compression using arithmetic codingInformation Processing & Management, 1992
- Optimal quantization by matrix searchingJournal of Algorithms, 1991
- A universal data compression systemIEEE Transactions on Information Theory, 1983
- Predictive Quantizing Systems (Differential Pulse Code Modulation) for the Transmission of Television SignalsBell System Technical Journal, 1966