Optimal joint power-rate adaptation for error resilient video coding
Why this work is in the frame
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Bibliographic record
Abstract
In recent years digital imaging devices become an integral part of our daily lives due to the advancements in imaging, storage and wireless communication technologies. Power-Rate-Distortion efficiency is the key factor common to all resource constrained portable devices. In addition, especially in real-time wireless multimedia applications, channel adaptive and error resilient source coding techniques should be considered in conjunction with the P-R-D efficiency, since most of the time Automatic Repeat-reQuest (ARQ) and Forward Error Correction (FEC) are either not feasible or costly in terms of bandwidth efficiency delay. In this work, we focus on the scenarios of real-time video communication for resource constrained devices over bandwidth limited and lossy channels, and propose an analytic Power-channel Error-Rate-Distortion (P-E-R-D) model. In particular, probabilities of macroblocks coding modes are intelligently controlled through an optimization process according to their distinct rate-distortion-complexity performance for a given channel error rate. The framework provides theoretical guidelines for the joint analysis of error resilient source coding and resource allocation. Experimental results show that our optimal framework provides consistent rate-distortion performance gain under different power constraints.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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