Fast Block Motion Estimation With 8-Bit Partial Sums Using SIMD Architectures
Bibliographic record
Abstract
In order to take advantage of the byte-type data parallelism in the existing single-instruction multiple-data (SIMD) technique, this paper introduces the concept of 8-bit partial sums, obtained by a 4-bit right-shift operation on the sum of the 16 luminance values in a column of a 16 x 16 block of a video frame. Since these partial sums are of only eight bits, eight of them can be processed concurrently in a single 64-bit SIMD register. A method of employing these partial sums in order to speed up a given block motion-estimation algorithm is then proposed. The notion of the 8-bit partial sums is extended to the four-level case. It is shown that there are 15 possible methods of utilizing these multilevel 8-bit partial sums to accelerate a block motion-estimation algorithm without any loss of accuracy of the algorithm. Each of these 15 methods is used in the full-search algorithm to determine the one that provides the lowest computational complexity. This method is adopted as the chosen scheme to accelerate various block motion-estimation algorithms. Extensive simulations are carried out on eight video sequences showing that substantial speed-up can be achieved when the chosen scheme is incorporated with the various motion-estimation algorithms. The simulation results also demonstrate that the implementation on SIMD architectures can further accelerate the execution of the proposed scheme by more than 93% percent.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".