Name
Abstract | ||
|---|---|---|
In this paper, a novel algorithm for low-power imagecoding and decoding is presented and the various inherenttrade-offs are described and investigated in detail. The algorithmreduces the memory requirements of vector quantization, i.e., thesize of memory required for the codebook and the number of memoryaccesses by using small codebooks. This significantly reduces thememory-related power consumption, which is an important part of thetotal power budget. To compensate for the loss of qualityintroduced by the small codebook size, simple transformations areapplied on the codewords during coding. Thus, small codebooks areextended through computations and the main coding task becomescomputation-based rather than memory-based. Each image block isencoded by a codeword index and a set of transformation parameters.The algorithm leads to power savings of a factor of 10 in codingand of a factor of 3 in decoding, at least in comparison toclassical full-search vector quantization. In terms of SNR, theimage quality is better than or comparable to that corresponding tofull-search vector quantization, depending on the size of thecodebook that is used. The main disadvantage of the proposedalgorithm is the decrease of the compression ratio in comparison tovector quantization. The trade-off between image quality and powerconsumption is dominant in this algorithm and is mainly determinedby the size of the codebook. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1023/A:1007945427023 | VLSI Signal Processing |
Keywords | Field | DocType |
Compression Ratio,Memory Access,Vector Quantization,Image Block,Effective Capacitance | Power budget,Linde–Buzo–Gray algorithm,Computer science,Algorithm,Image quality,Theoretical computer science,Coding (social sciences),Vector quantization,Code word,Decoding methods,Codebook | Journal |
Volume | Issue | ISSN |
18 | 1 | 0922-5773 |
Citations | PageRank | References |
1 | 0.40 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Masselos | 1 | 35 | 7.80 |
| P. Merakos | 2 | 11 | 4.59 |
| T. Stouraitis | 3 | 111 | 13.12 |
| C. E. Goutis | 4 | 97 | 14.78 |