Predictive classifier for image vector quantization

Abstract

A new scheme for a still image encoder using vector quantization (VQ) is proposed. The new method classifies the block into a suitable class and predicts both the classification type and the index information. To achieve better performance, the encoder decomposes images into smooth and edge areas by a simple method. Then, it encodes the two kinds of region using different algorithms to promote the compression efficiency. Mean-removed VQ (MRVQ) with block sizes 8 x8 and 16x16 pixels compress the smooth areas at high compression ratios. A predictive classification VQ (CVQ) with 32 classes is applied to the edge areas to reduce the bit rate further. The proposed prediction method achieves an accuracy ratio of about 50% when applied to the prediction of 32 edge classes. Simulation demonstrates its efficiency in terms of bit rate reduction and quality preservation. When the proposed encoding scheme is applied to compress the "Lena" image, it achieves the bit rate of 0.219 bpp with the peak SNR (PSNR) of 30.59 dB. (C) 2000 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(00)00908-9].