Image Coding Using Adaptive Classified Side-Match Finite-State Vector Quantization

Abstract

In this article, a new vector quantization (VQ) scheme called adaptive classified side-match finite-state vector quantization (ACSMVQ) is presented. This technique takes advantage of classified vector quantization (CVQ) and side-match vector quantization (SMVQ) so that the compression efficiency is much more attractive than traditional VQ techniques. In ACSMVQ, image blocks are classified into two main classes: edge blocks and non-edge blocks. For improving image quality, non-edge and edge blocks are further reclassified into different classes based on the properties of neighbouring blocks. Kirsh operators are utilized for the detection of edge properties within an image block. In order to synchronize the states in the encoder and decoder, the quantized upper and left blocks are exploited for classifying the input block both in the encoder and decoder. Therefore, no class index needs to be transmitted to the decoder. Moreover, the compression ratio can be further improved by applying small state code book. As shown in the experiment results, the average improvement of ACSMVQ over ordinary SMVQ is up to 2.7 dB at nearly the same bit-rate. Moreover, in comparison with ordinary VQ, the average improvement can be up to 4.24 dB at the same bit-rate.