Fast AWGN reduction in videos using change detection
Bibliographic record
Abstract
In this paper, two low-complexity filters, which are based on a novel filter structure consisting of both the spatial and temporal estimation processes and use a change detection technique to measure the interframe motion, are proposed to reduce additive white Gaussian noise (AWGN) in videos. Both the filters use the edge-adaptive Wiener filter for spatial estimation, while for temporal estimation two schemes are proposed, one based on the scalar Kalman filter and the other on the running average filter. These temporal estimation techniques are carded out on the spatial estimate to get the spatiotemporal estimate. The final estimate is obtained by a suitable adaptive combination of the spatial and the spatiotemporal estimates. The use of a change detection technique to measure the interframe motion results in a computational complexity, which is considerably lower than that of the existing filters using motion estimation and compensation technique. The qualitative and quantitative performance of the proposed filters are studied and compared with that of some of the existing ones. It is found that the proposed filters have much less processing time and also have better noise reduction ability compared to the others.
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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 | 0.001 |
| 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".