Low-Cost Method of Noise Estimation for Image and Video Coding
Bibliographic record
Abstract
Existing video and image codecs exploit spatial and temporal correlation to achieve compression, but presence of noise reduces this correlation and hence adversely effects their compression efficiency. Noise filtering of the input signal can significantly improve compression efficiency of video encoders. Temporal noise filtering is one such technique that can improve the compression efficiency of encoding noisy content by 5-10% with an AVI reference encoder. Temporal filtering relies on motion information and noise estimation for generating accurate filter coefficients. Generally, encoders have motion estimation modules which can be used for getting motion information, but noise estimation requires a special implementation. In this proposal, we present a low-cost noise estimation method which is used with temporal filtering in AV1 encoding. We calculate the difference between pixels in horizontal and vertical directions to generate the histograms of a difference map. A simple analysis of these histograms provides a very accurate measure of variance of the noise in frames. In comparison to the existing methods, this method has a very low computational overhead.
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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 | 0.001 | 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".