Decision trees for denoising in H.264/AVC video sequences
Bibliographic record
Abstract
All existing video coding standards are based on block-wise motion compensation and block-wise DCT. At high levels of quantization, block-wise motion compensation and transform produces blocking artifacts in the decoded video, a form of distortion to which the human visual system is very sensitive. The latest video coding standard, H.264/AVC, introduces a deblocking filter to reduce the blocking artifacts. However, there is still visible distortion after the filtering when compared to the original video. In this paper, we propose a non-conventional filter to further reduce the distortion and to improve the decoded picture quality. Different from conventional filters, the proposed filter is based on a machine learning algorithm (decision tree). The decision trees are used to classify the filter's inputs and select the best filter coeffcients for the inputs. Experimental results with 4 × 4 DCT indicate that using the filter holds promise in improving the quality of H.264/AVC video sequences.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".