A novel algorithm for low-power image and video coding

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...

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Published inIEEE transactions on circuits and systems for video technology Vol. 8; no. 3; pp. 258 - 263
Main Authors Masselos, K., Merakos, P., Stouraitis, T., Goutis, C.E.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.06.1998
Institute of Electrical and Electronics Engineers
Subjects
Applied sciences
Bit rate
Computational complexity
Decoding
Degradation
Energy consumption
Exact sciences and technology
Image coding
Image processing
Image quality
Information, signal and communications theory
Signal processing
Technological innovation
Telecommunications and information theory
Vector quantization
Video coding
Signal compression
Codebook
Vector quantization
Image processing
Coding
Video signal
Signal processing
Low power
Algorithm
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1051-8215
1558-2205
DOI:10.1109/76.678619