VQ Based on a Main Feature Classification in Images

Abstract

A number of algorithms have been developed for lossy image compression. Among the existing techniques, a block-based scheme is widely used because of its tractability even for complex coding schemes. Fixed block-size coding, which is the simplest implementation of block-based schemes, suffers from the nonstationary nature of images. The formidable blocking artifacts always appear at low bit rates. To suppress this degradation, variable block-size coding is utilized. However, the allowable range of sizes is still limited because of complexity issues. By adaptively representing each region by its feature, input to the coder is transformed to fixed-size (8×8) blocks. This capability allows lower cross-correlation among the regions. Input feature is also classified into the proper group so that vector quantization can maximize its strength compatible with human visual sensitivity. Bit rate based on this algorithm is minimized with the new bit allocation algorithm. Simulation results show a similar performance in terms of PSNR over conventional discrete cosine transform in conjunction with classified vector quantization.