Novel vector quantization based algorithms for low-power image coding and decoding

• Published in: IEEE transactions on circuits and systems. 2, Analog and digital signal processing Vol. 46; no. 2; pp. 193 - 198
• Main Authors: Masselos, K., Merakos, P., Stouraitis, T., Goutis, C.E.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.1999; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Computational complexity; Computer displays; Decoding; Degradation; Energy consumption; Exact sciences and technology; Image coding; Image processing; Image quality; Information, signal and communications theory; Signal processing; Telecommunications and information theory; Vector quantization; Video coding; Video compression; Image quality; System architecture; Experimental result; Codebook; Vector quantization; Image processing; Coding circuit; Decoding circuit; Low power; Computational complexity
• ISSN: 1057-7130; 1558-125X
• DOI: 10.1109/82.752952

In this paper, a novel scheme for low-power image coding and decoding based on vector quantization is presented. The proposed scheme uses small codebooks, and block transformations are applied to the codewords during coding. Using small codebooks, the proposed scheme has reduced memory requirements in comparison to classical vector quantization. The transformations applied to the codewords extend computationally the small codebooks compensating for the quality degradation introduced by the small codebook size. Thus the coding task becomes computation-based rather than memory-based, leading to significant power savings since memory-related power consumption forms the major part of the total power consumption of a system. Since the parameters of the transformations depend on the image block under coding, the small codebooks are dynamically adapted to the specific block under coding leading to acceptable image qualities. The proposed scheme 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. The main factor affecting both image quality and power consumption is the size of the codebook that is used.