Efficient Motion Vector Estimation and Coding for H.263-Based Very Low Bit Rate Video Compression
Bibliographic record
Abstract
In this paper, we propose an e#cient motion vector estimation and coding method for H.263-based low bit rate video compression. The method exploits structural constraints within the motion #eld. The same H.263 median predictor is used to localize motion estimation, which is directed to the best motion vectors by employing a computation-constrained layered search technique. Moreover, the localized motion #eld is encoded using semi-#xedlength codes. The resulting low bit rate video encoder yields essentially the same levels of rate-distortion performance and subjective qualityachieved byTelenor's implementation of the ITU TMN5 model. However, our motion vector estimation and coding method provides for substantially higher encoding speed and channel error robustness. # This work was supported by the Natural Sciences and Engineering Research Council of Canada. 1 1 Introduction Very low bit rate video compression techniques are becoming increasingly important due to present and...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".