A New Multihypothesis-Based Compressed Video Sensing Reconstruction System
Bibliographic record
Abstract
Multihypothesis-based compressed video sensing scheme attracts wide attention in the research of resource-constrained video application scenarios. However, high-accuracy weight prediction of hypotheses is always challenging especially for the high-motion sequences. To solve this problem, this paper proposes a novel multihypothesis-based distributed compressed video sensing (NMH-DCVS) system. The new multihypothesis system contains two components: hypotheses acquisition, and weight prediction. First, to acquire more high-quality hypotheses, a new hypotheses acquisition scheme is proposed by constructing the search window based on the temporal, and spatial correlation of the image blocks, respectively. The optimal matching block can be quickly determined. Second, to improve the accuracy of the multihypothesis weight prediction, a new residual transforming preprocessing-based weight prediction algorithm is proposed by transforming the original hypothesis set to residual hypothesis set. The influence of the quality fluctuation of the hypotheses on prediction accuracy is effectively suppressed. Moreover, the improved hypotheses further improve the sparsity of the residual hypothesis set, leading to the additional improvement of the accuracy of the proposed residual-based weight prediction algorithm. Experiment results show that compared with the state-of-the-art methods reported in the literature, the proposed new multihypothesis system significantly improves the decoding performance both in objective, and subjective quality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".