ADAPTIVE ENTROPY-CODED PRUNED TREE-STRUCTURED PREDICTIVE VECTOR QUANTIZATION OF IMAGES

In recent work, we described an adaptive entropycoded predictive vecto r quantization (PVQ) scheme for images which was shown to be capable o f excellent rate-distortion performance and to be surprisingly robust when applied to images outside the training set used in its design. Th is scheme made use of several entropy-constrained vector quantizers (E CVQ's), each with a corresponding Huffman encoder/decoder pair, embedd ed in a vector predictive feedback loop. The particular entropy-coded ECVQ in effect for any input image block depended upon the instantaneo us occupancy state of a buffer used to interface the resulting variabl e-length codewords to a fixed-rate transmission or storage channel. Th is entropy-coded PVQ scheme is a vector extension of previous work on adaptive entropy-coded predictive scalar quantization (PSQ); in partic ular, 2-D DPCM. The embedded ECVQ in this adaptive entropy-coded PVQ s cheme made use of a modification of a recently introduced design algor ithm, based upon clustering, which resulted in unstructured codebooks. Unfortunately, the computational complexity associated with this unst ructured embedded ECVQ can be substantial. In this paper we describe m uch simpler versions of this adaptive entropy-coded PVQ scheme where t he embedded ECVQ is replaced by a pruned tree-structured VQ (PTSVQ). T he resulting encoding scheme is shown to result in drastically reduced complexity at only a small cost in performance. We demonstrate coding results on selected real-world images.