Real-time video compression using entropy-biased ANN codebooks

Abstract

We describe hardware that has been built to compress video in real time using full-search vector quantization (VQ). This architecture implements a differential-vector-quantization (DVQ) algorithm which features entropy-biased codebooks designed using an artificial neural network (ANN). A special-purpose digital associative memory, the VAMPIRE chip, performs the VQ processing. We describe the DVQ algorithm, its adaptations for sampled NTSC composite-color video, and details of its hardware implementation. We conclude by presenting results drawn from real-time operation of the DVQ hardware. 1. INTRODUCTION Vector quantization has become well-known and widely studied since Shannon first established the merits of quantizing vectors rather than scalars. 1 Since that time, vector quantization (VQ) has been shown to be useful in the realm of data compression, particularly attracting attention for its efficient compression of digitized speech and image data. Meanwhile, as digital data has b..