Exploiting Orientational Redundancy in Multiview Video Compression
Bibliographic record
Abstract
This article introduces an approach for the acquisition and coding of multiview video. Multiview video systems consist of several cameras simultaneously capturing a single scene. Therefore a significant level of inter-view redundancy is present among the videos that can be exploited into video compression. Inspired by the idea of motion estimation in MPEG4 video compression, we introduce the idea of rotation estimation and compensation that is used in conjunction with motion estimation and compensation in order to remove spacial as well as temporal redundancies from the compressed video. The main question to be answered is how to choose the best sequence of compression among the frames when both time and space domains are involved. In this article, we model the above problem as a minimum cost graph traversal problem where cameras are considered as graph nodes and the cost of an edge connecting two cameras is inversely proportional to the similarity between the videos recorded by those cameras. We will then find the solution of this problem as the optimal traversal sequence that result in a high compression ratio.
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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 | 0.001 | 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 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".