Prediction and search techniques for RD-optimized motion estimation in a very low bit rate video coding framework
Why this work is in the frame
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Bibliographic record
Abstract
Prediction and search techniques are introduced for efficient rate-distortion optimized motion estimation in a very low bit rate video coding framework. For prediction, three types of predictors are considered: mean, weighted mean, and median. Prediction allows us to constrain the motion vector search to a small diamond-shaped area whose center is the predicted motion vector. The size of the search area is further constrained by employing a probabilistic model. We evaluate two models, both of which permit the contraction or the expansion of the search area as a function of the local statistics of the motion flow. The proposed techniques are analyzed in the context of a very low bit rate DCT-based video coding framework, where a rate-distortion criterion is used for motion estimation as well as for 8/spl times/8 block coding mode selection. A particular resulting very low bit rate video coder is shown experimentally to outperform the H.263 TMN5 simulation model in terms of encoding speed and compression performance, simultaneously.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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