Bibliographic record
Abstract
The Distributed Video Coding (DVC) follows an approach different from the conventional video coding. DVC has a simpler encoder but a more complicated decoder. This feature makes it possible to encode video in computation and energy constrained devices. Thus, DVC is appealing in sensor networks and other wireless networks. When transmitting real-time DVC encoded video streams, in order to adjust coding parameters according to the time-varying communication channel conditions or the dynamics of available bandwidth in the bottleneck, the source needs an efficient way to know the tradeoff of the coding parameters and the decoded video quality. However, how to quantify the DVC video quality using tractable models is an open issue. In this paper, we propose a distortion analysis model for DVC encoded Wyner-Ziv frames. The proposed closed-form distortion model for Wyner-Ziv frames is based on the reconstruction method of the "nearest neighbor binning". With the distortion analysis model, the average video frame PSNR can be estimated as a function of the codec parameters and the video statistics. Extensive simulations with different types of videos have been conducted and the results validate the accuracy of the proposed model. The model will be an enabling tool to further optimize the system parameters and network protocols for supporting DVC coded video over wireless and wired networks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".