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Adaptive entropy-coded predictive vector quantization of images

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2 Author(s)
Modestino, J.W. ; Dept. of Electr. Comput. & Syst. Eng., Rensselaer Polytech. Inst. Troy, NY, USA ; Yong Han Kim

The authors consider 2-D predictive vector quantization (PVQ) of images subject to an entropy constraint and demonstrate the substantial performance improvements over existing unconstrained approaches. They describe a simple adaptive buffer-instrumented implementation of this 2-D entropy-coded PVQ scheme which can accommodate the associated variable-length entropy coding while completely eliminating buffer overflow/underflow problems at the expense of only a slight degradation in performance. This scheme, called 2-D PVQ/AECQ (adaptive entropy-coded quantization), is shown to result in excellent rate-distortion performance and impressive quality reconstructions of real-world images. Indeed, the real-world coding results shown demonstrate little distortion at rates as low as 0.5 b/pixel

Published in:

Signal Processing, IEEE Transactions on  (Volume:40 ,  Issue: 3 )