Compressed domain motion vector resampling for downscaling of MPEG video
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digital video databases in compressed form are becoming widely available. In applications such as video browsing, and picture in picture, for a lower bitrate, there is a need to down-sample the video before transmission. The conventional approach to downscale a compressed video sequence is to decompress it at the video server, perform the down-sampling in the pixel domain and then recompress it for efficient delivery. This process is computationally intensive due to the motion estimation process required during the recompression phase. In the alternative compressed domain approach, the motion vectors of the downscaled video sequence are computed directly from the motion vectors of the original full size stream. In this paper we propose a compressed domain technique that generates a better estimate for the downscaled motion vectors. Simulations suggest that the performance achieved with the proposed method is superior by up to 1 dB to the current compressed domain techniques.
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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 | 0.000 |
| Open science | 0.001 | 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