<title>Image vector quantization with channel coding</title>

Abstract

In this work, we present several methods of reduction of specific distortions such as `blocking effect', `staircase structure' and `details blurring' accompanying vector quantization (VQ) process in coding both still and moving pictures. These methods include: (1) the overlap method in forming and rebuilding blocks (vectors); (2) the classified vector quantization based on a composite source model supplying with better perceptual quality of reconstructed images; (3) adaptive VQ with Discrete Cosine-III Transform, which gives better performances than VQ with standard Discrete Cosine Transform; (4) the 3D vector quantizer giving the possibility of exploiting both intra- and inter-frame correlations in image sequence coding and thus resulting in higher reconstruction quality, even at a very low bit rate. Moreover, a large codebook built partially outside training is used to encode the image sequence. The 3D VQ gives the possibility of reducing the annoying floating noises in image sequence coding. At last we proposed an algorithm for VQ with channel coding (for Gaussian channel). In the first experiment we used Phase Shift Keying (PSK), in the other experiment we used Minimum Shift Keying and error correcting convolutional codes (66,35) which gives superbly better performances than with PSK.