Effect of scaling on the area and performance of the H.264/AVC full-search fractional motion estimation algorithm on field-programmable gate arrays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it