Title of article :
Conditional entropy coding of VQ indexes for image compression
Author/Authors :
Xiaolin Wu، نويسنده , , Jiang Wen، نويسنده , , Wing Hung Wong، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 1999
Abstract :
Block sizes of practical vector quantization (VQ)
image coders are not large enough to exploit all high-order
statistical dependencies among pixels. Therefore, adaptive entropy
coding of VQ indexes via statistical context modeling can
significantly reduce the bit rate of VQ coders for given distortion.
Address VQ was a pioneer work in this direction. In this paper
we develop a framework of conditional entropy coding of VQ
indexes (CECOVI) based on a simple Bayesian-type method of
estimating probabilities conditioned on causal contexts. CECOVI
is conceptually cleaner and algorithmically more efficient than
address VQ, with address-VQ technique being its special case. It
reduces the bit rate of address VQ by more than 20% for the
same distortion, and does so at only a tiny fraction of address
VQ’s computational cost.
Keywords :
Address VQ , entropycoding , context modeling of images , joint probability estimation , vector quantization.
Journal title :
IEEE TRANSACTIONS ON IMAGE PROCESSING
Journal title :
IEEE TRANSACTIONS ON IMAGE PROCESSING