An all digital implementation of a modified Hamming net for video compression with prediction and quantization circuits

Abstract

The hardware and algorithms used to vector quantize predicted pixel intensity differences for real-time video compression are described. The hardware is designed for rapid vector quantization (VQ) performance, which entails the development of application-specific associative memory circuits. A modified DPCM algorithm is originally examined to determine how neural circuitry could enhance its operation. It was determined that quantization and encoding could be improved by consolidating these two functions into one, and by increasing the amount of information (i.e. number of pixels) quantized at a time. The result is a predictive scheme that vector quantizes differential values. Some of the disadvantages of VQ algorithms are solved using associative memories. The video compression algorithm and the associative memory design are described.<>