An Efficient Motion Estimation Method for H.264-Based Video Transcoding with Spatial Resolution Conversion
Why this work is in the frame
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Bibliographic record
Abstract
Motivated by the wide adoption of H.264 and the demand of universal multimedia data access over the expanding network with diverse devices, this paper studies H.264-based video transcoding with spatial resolution conversion. First, a practical solution for efficiently determining a reference frame is proposed to take advantage of the new feature of multiple references in H.264. Then, a motion vector estimation algorithm based on a multiple linear regression model is proposed to utilize the motion information in the original scenes for efficiently predicting motion vectors in the down-scaled scene. Experimental results show that, compared with a benchmark solution, the proposed method significantly reduces the transcoding complexity by 16 times while maintaining comparable rate distortion performance with a decrease of 0.06 dB in PSNR and 4% increase in the bit rate.
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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 | 0.001 | 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 it