Model Based Image Compression and Adaptive Data Representation by Interacting Filter Banks

Part of Advances in Neural Information Processing Systems 2 (NIPS 1989)

Authors

Toshiaki Okamoto, Mitsuo Kawato, Toshio Inui, Sei Miyake

Abstract

introduced. Based on

To achieve high-rate image data compression while maintainig a high quality reconstructed image, a good image model and an efficient way to represent the specific data of each image must be the physiological knowledge of multi - channel characteristics and interactions between them in the human visual system, a mathematically coherent parallel architecture for image data compression which utilizes the Markov Image model and interactions between a vast number of filter banks, is proposed.