Lossless compression of continuous-tone images via context selection, quantization, and modeling

被引:173
作者
Wu, XL
机构
[1] Department of Computer Science, University of Western Ontario, London
基金
加拿大自然科学与工程研究理事会;
关键词
D O I
10.1109/83.568923
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:656 / 664
页数:9
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