Effect of scaling on the area and performance of the H.264/AVC full-search fractional motion estimation algorithm on field-programmable gate arrays
Bibliographic record
Abstract
Fractional motion estimation (FME) is an important part of the H.264/AVC video encoding standard. The algorithm can significantly increase the compression ratio of video encoders while preserving high video quality. The full-search FME algorithm, however, is computationally expensive and can consist of over 45% of the total motion estimation process. To maximise the performance and efficiency of FME implementations on field-programmable gate arrays (FPGAs), one needs to efficiently exploit the inherent parallelism in the algorithm. The authors investigate the scalability of the full-search FME algorithm on FPGAs and also implemented six scaled versions of the algorithm on Xilinx Virtex-5 FPGAs. The authors found that scaling the algorithm vertically within a 4×4 sub-block is more efficient than scaling horizontally across several sub-blocks. It is shown that, with four reference frames, the best vertically scaled design can achieve 96 frames-per-second (fps) performance while encoding full 1920×1088 progressive HDTV video, and the design only consumes 25.5 K LUTS and 28.7 K registers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".