An Efficient Multiple Description Coding Scheme for the Scalable Extension of H.264/AVC (SVC)
Why this work is in the frame
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Bibliographic record
Abstract
The demand for efficient scalable video codecs has constantly been on the rise in response to the increase in the variety of services and QoS requirements in multimedia networks. Existing standardization efforts, such as the scalable video coding extension of the H.264/AVC standard, do not offer efficient error resilient protection for all the different levels of video enhancement. We developed a multiple description scalable video coding technique that offers complementary and independently decodable descriptions, offering acceptable video quality even if only one of them is successfully received. Performance evaluations show that our scheme delivers an average improvement of 5 dB for single channel decoding and an improvement of 2 dB on average for packet loss simulations when compared with the UXP protected SD-SVC and the multiple-description motion compensated temporal filtering (MD-MCTF) scheme at comparable redundancy levels
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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