LOSSLESS COMPRESSION OF CONTINUOUS-TONE IMAGES VIA CONTEXT SELECTION,QUANTIZATION, AND MODELING

Context modeling is an extensively studied paradigm for lossless compr ession of continuous-tone images. However, without careful algorithm d esign, high-oi der Markovian modeling of continuous-tone images is too expensive in both computational time and space to be practical, Furth ermore, the exponential growth of the number of modeling states in the order of a Markov model can quickly lead to the problem of context di lution; that is, an image may not have enough samples for good estimat es of conditional probabilities associated,vith the modeling states, I n this paper, new techniques for context modeling of DPCM errors are i ntroduced 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 probl em 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 perf ormance and yet remains practical.