Distributed video coding supporting hierarchical GOP structures with transmitted motion vectors
Bibliographic record
Abstract
Abstract In this paper, we propose a new distributed video coding (DVC) method, with hierarchical group of picture (GOP) structure. Coding gain of DVC can be significantly improved by enlarging GOP size for slow-moving frames. The proposed DVC decoder estimates a side information (SI) frame and transmits motion vectors (MVs) of the SI to the proposed encoder. Using the received MVs from the decoder, the proposed encoder can generate a predicted SI (PSI), which is the same as the SI in the decoder, and estimate the quality of PSI with minimal computational complexity. The proposed method decides the best coding mode among key, Wyner-Ziv (WZ), and skip modes, by estimating rate-distortion costs. Based on the selected best coding mode, the best GOP size can be automatically determined. As the GOP size is adaptively decided depending on the SI quality, entropy and parity bits can be effectively consumed. Experimental results show that the proposed algorithm is around 0.80 dB better in Bjøntegaard delta (BD) bitrate than an existing conventional DVC system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".