A texture replacement method at the encoder for bit-rate reduction of compressed video
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Open science | 0.001 | 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 it