Neural Approach for TV Image Compression Using a Hopfield Type Network

ABSTRACT A self-organizing Hopfield network has been developed in the context of Vector Ouantiza -tion, aiming at compression of television images. The metastable states of the spin glass-like network are used as an extra storage resource using the Minimal Overlap learning rule (Krauth and Mezard 1987) to optimize the organization of the attractors. The sel f-organi zi ng scheme that we have devised results in the generation of an adaptive codebook for any qiven TV image. As in many applications they are unknown, the aim of this work is to develop a network capable to learn how to select its attractors. TV image compression using Vector Quantization (V.Q.)(Gray, 1984), a key issue for HOTV transmission, is a typical case, since the non neural algorithms which generate the list of codes (the codebookl are suboptimal.