Title

LVQ of Image Sequence Source and ANS Classification of Finite State Machine for High Compression Coding

Document Type

Paper

Publication Date

12-1-1990

Abstract

Artificial neural system (ANS) classification has been applied to the total set of states of a finite-state machine operating as part of an image-sequence coder. It has been found that the classification of states allows the use of a much smaller number of representation states, thereby drastically reducing the storage requirements of the finite-state machine. The coder implements a scheme for high compression of teleconferencing image-sequence data. It utilizes neural-net-based learning vector quantization (LVQ) operating in the spatial domain on 16-dimensional vectors. The method is structured as a combination of an intraframe algorithm followed by an interframe algorithm, operating on a bundle of frames. The intraframe algorithm operates on the head frame of the bundle; the interframe algorithm follows in order to encode the remaining frames. Then, the encoding process repeats with a new bundle of frames. The intraframe and the interframe algorithms are finite state-based. Simulation experiments have been carried out for a videoconferencing image sequence consisting of 20 frames of 112 × 96 pixels, with 25% average block motion. The representation vectors were of 2 × 2 × 4 resolution. The results obtained have shown that for peak signal-to-noise ratio (PSNR) = 32 dB, the required bit rate is 0.08 to 0.10 b/pixel.

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