A texture replacement method at the encoder for bit-rate reduction of compressed video
Bibliographic record
Abstract
We propose a method for texture replacement in video sequences. Our method, which is applied at the encoder side, consists of removal of texture from selected regions of the original frames, synthesis of new texture, and mapping of the new texture back onto the segmented regions. The texture removal stage employs highly effective color-based angular maps. The texture analysis and texture synthesis stages make use of steerable pyramids. The latter stage also employs constraints that are derived using a vocabulary and grammar for color pattern similarity evaluation that have been introduced previously. Because they have different characteristics than those of the original textures, the synthesized textures can be coded more effectively. Consequently and most importantly, significantly reduced bit rates of the compressed video sequences with the texture replaced are obtained as compared to those of the original sequences. Moreover, because the synthesized textures have similar perceptual characteristics to those of the original textures, the video sequences with the texture replaced are also visually similar to the original sequences. Even more, because it is performed at the encoder and it does not have any impact on the decoder, our texture replacement method is cost effective. We illustrate its performance and computational efficiency using movie sequences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".