Progressive scalable interactive region-of-interest image coding using vector quantization

摘要

We have developed novel progressive scalable region-of-interest (ROI) image compression schemes with rate-distortion-complexity tradeoff based on vector quantization. Residual vector quantization (RVQ) equips the encoder with a multi-resolution apparatus which is useful for rate-distortion tradeoff. Having all advantages of RVQ, jointly suboptimized RVQ provides a distortion-complexity adjustment. The systems are unbalanced in the sense that the decoder has less computational requirements than the encoder. The proposed jointly suboptimized RVQ method provides an interactive tool for fast ROI-based browsing from image archives.