Side information generation using optical flow and block matching in Wyner-Ziv video coding
Bibliographic record
Abstract
In this work, a new algorithm to generate high quality side information in Wyner-Ziv video coding is proposed. A block-matching algorithm is incorporated into the forward and backward optical flow and warping algorithms to find the forward and backward motion fields that are used for frame interpolation. Also, a symmetric optical flow algorithm for the purpose of frame interpolation is obtained by parameter modification in the energy functional of an optical flow algorithm. The average of the interpolated frames estimated using the forward/backward motion fields and symmetric flow is used to provide a high quality side information frame for decoding of the corresponding Wyner-Ziv frame in the Wyner-Ziv video coding problem. The proposed algorithm significantly improves the quality of the side information frames compared with those provided by the advanced block matching frame interpolation in the typical Wyner-Ziv video codecs. Simulation results showing significant improvements in side information quality and rate-distortion performance in Wyner-Ziv video coding are provided.
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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 | 0.000 | 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 | 0.001 |
| 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".