Image Compression by Perceptual Vector Quantization

This paper describes a technique to compress images based on vector quantization. The vector quantizer is designed to reduce both perceptual irrelevancy and mathematical redundancy. This is done without using transforms and entropic coding, which are normally used respectively prior and after quantization. Because of its structure, the vector quantizer can be implemented efficiently as a uniform product vector quantizer using a variable quantization step. The quantization step is computed adaptively to track and exploit local features present in the image.