Image vector quantization with a perceptually-based cell classifier

Vector quantization (VQ) has made it possible to utilize perceptually meaningful techniques for direct space-domain image coding. A simple 2 or 3 way classified codebook approach [2,3] allocates the perceptually important edges with more resolution than the easily encoded monotone regions of an image. In this paper, we introduce a major extension of the classification approach to include edge orientation and location, thereby exploiting an important feature of the human visual mechanism. In particular, each 4 × 4 block of pixels is classified into one of 31 classes for the case of 16 dimensional VQ. The encoding and codebook design complexity is significantly reduced, allowing us to use large codebooks designed from a large database of training images. We present images encoded at 0.7 and 0.8 bits per pixel using this scheme with 16- dimensional vectors. Only a small fraction of one bit per pixel is needed to code the monotone regions of an image; the rest of the bitrate is used to achieve a high level of edge integrity.