LVQ of image sequence source and ANS classification of finite state machine for high compression coding
CONSTANTINE N. MANIKOPOULOS, Grigoris Antoniou, S. Metzelopoulou · 1990
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. These 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 of2×2×4 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