An embedding framework for video reconstruction using Gaussian mixture models
Bibliographic record
Abstract
In this paper, we propose a novel video reconstruction methodology which is built based on alternating direction method of multipliers (ADMM) algorithm. In this regard, a new enhanced ADMM model has been used which permits the user to apply image or video reconstruction techniques as sub-problems being embedded to a denoising methodology. Correspondingly, we use conventional compressive sensing (CS) based Gaussian mixture models (GMM) as a subproblem of our proposed framework. On the other hand, sparse 3D transform-domain block matching (BM3D) is used as the denoiser of algorithm in order to remove the remaining artifacts and noise in the reconstructed video frames. Consequently, by considering both online and offline CS-based GMM frameworks, we are able to make two forms of GMM based video reconstruction algorithms which are represented as online and offline structures. Using the proposed algorithms, video reconstruction is more satisfactory in terms of visual quality in comparison with other state of the art techniques.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".