Prediction-Based Flexible Triangle Search Algorithm for Block Based Motion Estimation
Bibliographic record
Abstract
This paper presents a technique that predicts the initial search direction for the flexible triangle search algorithm (FTS), which was introduced in previous work by the authors. The FTS algorithm is a fast block-matching algorithm for block-based motion estimation. In the FTS, a search triangle is used to find the best matching blocks between two frames through successive iterations. During the search, the triangle changes its direction and size using reflection, expansion, contraction, and translation operations. These operations provide the triangle with the necessary flexibility to perform coarse or fine search and to locate the best matching blocks while checking fewer search positions compared to most other search algorithms. Analysis of the FTS behavior showed that the proper selection of the starting triangle for the search reduces the required number of block-matching evaluations by directing the search earlier in the direction of the minimum. In this paper, a prediction-based FTS, the PFTS, is introduced. In the PFTS, a prediction step is added to obtain the initial triangle for the search. Simulation results indicate that the PFTS requires a smaller number of block matching operations than that of the FTS. In addition, the compression ratio was improved slightly while the visual quality of the reconstructed sequence remained the same compared to the FTS
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".