A novel algorithm for low-power image and video coding

Abstract

A novel scheme for low-power image and video coding and decoding is presented. It is based on vector quantization, and reduces its memory requirements, which form a major disadvantage in terms of power consumption. The main innovation is the use of small codebooks, and the application of simple but efficient transformations to the codewords during coding to compensate for the quality degradation introduced by the small codebook size. In this way, the small codebooks are computationally extended, and the coding task becomes computation based rather than memory based, leading to significant power consumption reduction. The parameters of the transformations depend on the image block under coding, and thus the small codebooks are dynamically adapted each time to this specific image block, leading to image qualities comparable to or better than those corresponding to classical vector quantization. The algorithm leads to power savings of a factor of 10 in coding and of a factor of 3 in decoding at least, in comparison to classical full-search vector quantization. Both image quality and power consumption highly depend on the size of the codebook that is used.

ESVD: An Integrated Energy Scalable Framework for Low-Power Video Decoding Systems

“…For example, given a coded frame, after entropy decoding, the macroblock information is extracted as follows, the type is intra, the partition belongs to 8 × 8, the position lies in central adjacency region, and L j (i) = i still comes into existence. Then the finial marked coefficient is calculated through (4). It means that the labeling energy index of this macroblock belongs to the corresponding energy profile.…”