Novel scheme for low-power classified vector quantisation image coding

Abstract

A novel scheme for low-power image coding and decoding based on classified vector quantisation is presented. The main idea is the replacement of the memory accesses to large background memories (most power-consuming operations), by arithmetic and/or application-specific computations. Specifically, the proposed image coding scheme uses small sub-codebooks to reduce the memory requirements and memory-related power consumption in comparison with classical vector quantisation schemes. By applying simple transformations on the codewords during coding, the proposed scheme extends the small sub-codebooks, compensating for the quality degradation introduced by their small size. Thus, the main coding task becomes computation-based rather than memory-based, leading to a significant reduction in power consumption. The proposed scheme achieves image qualities comparable with, or better than, those of traditional vector quantisation schemes, as the parameters of the transformations depend on the image block under coding, and the small sub-codebooks are dynamically adapted each time to this specific image block. The main disadvantage of the proposed scheme is the decrease in the compression ratio in comparison with classical vector quantisation. A joint (quality–compression ratio) optimisation procedure is used to keep this side-effect as small as possible.