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 with
the modeling states. In this paper, new techniques for context
modeling of DPCM errors are introduced that can exploit contextdependent
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.
