Error-Resilient and Error Concealment 3-D SPIHT for Multiple Description Video Coding With Added Redundancy
Bibliographic record
Abstract
In this paper, we present a multiple description video coding algorithm based on error-resilient and error concealment set partitioning in hierarchical trees (ERC-SPIHT). In this proposed approach, additional redundancy is generated by wavelet decomposing the spatial root subband and such redundancy is then intentionally inserted into the substreams. As a result, the novelty of the proposed approach is that the root subband coefficients lost during transmission in any substream can be reconstructed by exploiting both inherent redundancy and inserted redundancy. This reconstruction procedure is implemented in two steps, first by using existing 2-D error concealment techniques, and second with the proposed root subband recovery approach. The former step is used to estimate the missing coefficients in the spatial root and high frequency subbands by exploiting the inherent redundancy, while the latter attempts to utilize the inserted redundancy to further improve the precision in the estimation of the missing spatial root subband coefficients. The proposed root subband recovery method can be iteratively applied and accuracy of the reconstruction can be gradually increased with each iteration. Experimental results on different video sequences show that the proposed method maintains error-resilience with high coding efficiency. In particular, our results demonstrate that the proposed algorithm achieves a significant improvement on video quality by up to 2.5753 dB in the presence of a substream loss compared to ERC-SPIHT.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".