Yh. Kim et Jw. Modestino, ADAPTIVE ENTROPY-CODED PRUNED TREE-STRUCTURED PREDICTIVE VECTOR QUANTIZATION OF IMAGES, IEEE transactions on communications, 41(1), 1993, pp. 171-185
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.