Color correction of multiview video with average color as reference
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
When capturing multiview video, there can be significant variations in the color of views captured with different cameras. This negatively affects compression efficiency when multiview video is coded using inter-view prediction. In this paper we propose a method for correcting the color of multiview video sets as a preprocessing step to compression. Unlike previous work where one of the captured views is used as the color reference, we correct all views to match the average color of the set of views. Block based disparity estimation is used to find matching points between all views in the video set, and the average color is calculated for these matching points. Least squares regressions are used to find functions that will make each view match the average color. Experimental results show that the proposed method results in video sets that closely match in subjective color. Furthermore, when multiview video is compressed with JMVM, the proposed method increases compression efficiency by up to 1.0 dB compared to compressing the original uncorrected video.
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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 | 0.000 | 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 | 0.001 | 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