Memory reduction and image quality enhancement method for classified vector quantization

This paper considers new memory reduction and image quality enhancement method for classified vector quantization (CVQ) using symmetry reflection, rotation and inversion of edge subimages. These are used to join appropriate edge classes thus reducing memory requirements for edge codebooks by 4(8) times. Besides the memory reduction and increases of PSNR for images outside the training set our method also relieves codebook generation for high bit rate by reducing the number of images that should be inside the training set. The proposed method has been tested with two different classification methods in order to ensure generality of the method.