ADAPTIVE ENTROPY-CODED PREDICTIVE VECTOR QUANTIZATION OF IMAGES

We consider 2-D predictive vector quantization (PVQ) of images subject to an entropy constraint and demonstrate the substantial performance improvements over existing unconstrained approaches. Furthermore, we 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, is shown to result in excellent rate-distortion performance and impressive quality reconstructions on real-world images. Indeed, the real-world coding results shown here demonstrate little distortion at rates as low as 0.5 b/pixel.