Rate distortion bounds for blocking and intra-frame prediction in videos
Bibliographic record
Abstract
Recently we proposed a block-based conditional correlation coefficient model for natural videos in the spatial-temporal domain. The conditioning is on local texture and the optimal parameters can be calculated for a specific video with a mean absolute error (MAE) usually smaller than 5%. We used this conditional correlation model and the classic results on conditional rate distortion functions to calculate new theoretical rate distortion bounds for videos which appear to be the only valid theoretical rate distortion bounds with regard to the current cutting-edge video compression technologies such as those standardized in AVC/H.264. In this paper, we focus on utilizing the new block-based local-texture-dependent correlation model to derive rate distortion bounds for blocking and optimal prediction across neighboring blocks. We study the penalty paid in average rate when the correlation among the neighboring blocks is discarded completely or is incorporated partially through predictive coding. We calculate the thresholds in average rate and distortion when incorporating the correlation among the neighboring blocks through optimal predictive coding becomes worse than completely discarding this correlation. We also discuss the role of local texture in inter-frame prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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".