Name
Abstract | ||
|---|---|---|
In the H.264/AVC video coding standard, the mode selection part requires a considerable amount of computation and memory access. This paper presents a fast or computationally efficient mode prediction and selection approach which has the following attributes: (a) both the spatial and temporal information are used to achieve early termination using adaptive thresholds, (b) inclusion of a modulator capable of trading off computational efficiency and accuracy and (c) a homogenous region detection procedure for 8times8 blocks based on adaptive thresholds. It is shown that the above attributes lead to a computationally efficient video coding implementation as compared to the previous fast approaches. The results obtained on QCIF, CIF, and HD format video sequences based on two different implementation platforms JM12 and x264 are presented to show the computational efficiency of the developed approach at the expense of acceptably low losses in video quality. Improvement in memory access performance is also demonstrated using a DSP processor simulator. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TCE.2008.4560174 | IEEE Trans. Consumer Electronics |
Keywords | Field | DocType |
different implementation platform,developed approach,computationally efficient mode selection,avc video coding,video quality,avc video,computational efficiency,adaptive threshold,memory access,computationally efficient mode prediction,hd format video,computationally efficient video,histograms,adaptive thresholding,computational complexity,indexing terms,digital signal processing,computational modeling,encoding | Histogram,Computer vision,Context-adaptive variable-length coding,Computer science,Coding (social sciences),Artificial intelligence,Video quality,Computation,Scalable Video Coding,Encoding (memory),Context-adaptive binary arithmetic coding | Journal |
Volume | Issue | ISSN |
54 | 2 | 0098-3063 |
Citations | PageRank | References |
13 | 0.94 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianfeng Ren | 1 | 291 | 16.97 |
| N. Kehtarnavaz | 2 | 264 | 33.56 |
| M. Budagavi | 3 | 70 | 8.39 |