New method for reducing GOP-boundary artifacts in wavelet-based video coding
Bibliographic record
Abstract
The conventional motion-compensated temporal wavelet transform using the 5/3 filter produces GOP-boundary artifacts, i.e., a drop in picture quality at the boundaries of groups of pictures (GOP). A simple and efficient method is proposed in this paper to reduce the GOP-boundary artifacts and improve the overall performance of the transform. With this method, a group of pictures to be temporally transformed is extended by boundary repetition to one side and by symmetrical extension to the other side. The sub-sampling rule changes from one level to another during the multi-level transform. In addition, a non-uniform quantization scheme is employed. This method does not require any additional computation or memory. Experimental results of video coding with six test sequences show that this method outperforms the conventional method, providing a coding gain of 0.27 dB in terms of average PSNR and up to 1.95 dB in terms of minimum PSNR
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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 | 0.001 |
| 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".