An efficient low random-access delay panorama-based multiview video coding scheme
Why this work is in the frame
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Bibliographic record
Abstract
We present an efficient low random delay scheme for multiview video coding (MVC). In the proposed scheme, inter-view prediction (disparity estimation), which introduces time-consuming computations and random access delay to MVC, is replaced with a residue-stream coding process. Our algorithm transforms the middle view to a panoramic view of the scene. Then the residue streams are created as the difference of the luma and chroma values of overlapping regions of each view and the panoramic view. Finally the panoramic stream and all residue streams are encoded separately (simulcast coding). The hierarchical B picture prediction structure is implemented for coding each stream. Performance evaluations show that our proposed coding method outperforms the recent multiview video coding standard by up to 2.13 dB PSNR and enhances the compression ratio by 24.6%, while reducing random-access delay by 50%.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 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