Efficient two step edge based partial distortion search for fast block motion estimation
Bibliographic record
Abstract
In video coding, block based full search motion estimation algorithm has been widely used, but it suffers from high computational requirements. In order to reduce the computations, this paper proposes a novel edge based partial distortion search (EPDS) algorithm which reduces the computation of each distortion measure by using partial distortion search. In this algorithm, the entire macroblock (MB) is divided into different sub-blocks and the calculation order of partial distortion is determined based on the edge strength of sub-blocks. This algorithm adaptively changes the early termination threshold for every accumulated partial sum of absolute difference. In the proposed method, only selected numbers of search points are considered for candidate motion vectors. An efficient early termination method, which is based on the dynamic threshold, is also proposed to decide whether a search point has met the Rate-Distortion (RD) cost criterion so that the best search point can be determined early. Simulation results show that the proposed method offers a remarkable improvement in computational speed when compared to full search (FS) and normalized partial distortion search (NPDS) algorithms. The proposed method is 115 times faster than FS, 10 times faster than NPDS and 2 times faster than the dual halfway stop NPDS (DHS-NPDS) on an average. PSNR degradation of the proposed algorithm is negligible and in the region of 0.01 dB. The proposed method can be easily applied to many mobile video application areas such as digital cameras and DMB (Digital Multimedia Broadcasting) phones1.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".