Analysis and architecture design of scalable fractional motion estimation for H.264 encoding
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
<p>FractionalMotion Estimation (FME) is an important part of the H.264/AVC video encoding standard. FME can significantly increase the compression ratio achievable by video encoders while improving video quality. However, it is computationally expensive and can consist of over 45% of the total motion estimation runtime. To maximize the performance and hardware utilization of FME implementations on Field-Programmable Gate Arrays (FGPAs), one needs to effectively exploit the inherent parallelism in an algorithm. In the work we explore two approaches to FME algorithm parallelization in order to effectively increase the processing power of the computing hardware. The first method is referred to as vertical scaling and the second horizontal scaling. In total, we implemented six scaled FME designs on a Xilinx Virtex-5 FPGA. We found that our best scaled FME design exhibited a speedup of 8x over the horizontally scaled designs. Additionally, we conclude that scaling vertically within 4x4 pixel sub-block is more efficient than scaling horizontally across several sub-blocks. As a result we were able to achieve higher video resolutions at lower resource costs. In particular, it is shown that the best vertically scaled design can achieve 30 fps of QSXGA (2560x2048) video using 4 reference frames with only 25.5L LUTS and 28.7K registers.</p>
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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