Checksum-Filtered List Decoding Applied to H.264 and H.265 Video Error Correction
Bibliographic record
Abstract
The latest video coding standards, H.264 and H.265, are highly vulnerable in error-prone networks. Reconstructed packets may exhibit significant degradation in terms of peak signal-to-noise ratio and visual quality. This paper presents a novel list-decoding approach exploiting the receiver side user datagram protocol (UDP) checksum. The proposed method identifies the possible locations of errors by analyzing the pattern of the calculated UDP checksum. This permits considerably reducing the number of candidate bitstreams in comparison to conventional list decoding approaches. When a packet composed of N bits contains a single-bit error, instead of considering N candidate bitstreams, as is the case in conventional list decoding approaches, the proposed approach considers N/32 candidate bitstreams, leading to a reduction of 97% of the number of candidates. For a two-bit error, the reduction increases to 99.6%. The method's performance is evaluated using H.264 and H.265 test model software. Our simulation results reveal that, on average, the error was corrected perfectly 80%-90% of the time (the original bitstream was recovered). In addition, the proposed approach provides, on average, a 2.79-dB gain over frame copy (FC) error concealment using the joint model and a 3.57-dB gain over our implementation of FC error concealment in the High Efficiency Video Coding test model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".