A new lower bound for fast block motion estimation algorithms
Bibliographic record
Abstract
Current video coding standards of H.263, MPEG-2, and MPEG-4 employ the block motion estimation technique to decorrelate video sequences. Since the full-search algorithm for block motion estimation has a great deal of computational complexity, it is not suitable for real-time implementations. Some fast block motion estimation algorithms, such as the selective elimination algorithm, the multilevel successive elimination algorithm, and the vector-based algorithm, have been proposed in the literature. These algorithms reduce the computational complexity of the full search algorithm by utilizing a lower bound for the block-matching criterion of the mean absolute difference (MAD). However, the partial sums for the computation of the lower bound in these algorithms cannot take advantage of the byte-type data parallelism in the existing single instruction multiple data (SIMD) technique. In this paper, a new lower bound for the MAD is established, and this is achieved with the following three objectives in mind: (i) the partial sums can be used to calculate a lower bound to discard the computation of the MAD, (ii) the partial sums are of only 8 bits and saved in a contiguous memory space, and (in) eight partial sums can be processed concurrently in a single 64-bit SIMD register. The new lower bound can be employed in conjunction with an existing block motion estimation algorithm to accelerate the execution of the algorithm without any loss of accuracy. Simulation results demonstrate that the above scheme can accelerate the execution of the vector-based fast algorithm, selective elimination algorithm, and full search algorithm by about 10, 20, and 40 percents, respectively.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".