Computationally efficient tone-mapping of high-bit-depth video in the YCbCr domain
Bibliographic record
Abstract
High dynamic range (HDR) video content is able to provide superior picture quality. This is because the representation of HDR signals requires more bits than the 8-bit low dynamic range (LDR) video. Tone-mapping is the process that converts HDR to LDR signals. Most tone-mapping methods are derived only for the luminance component. This mapping function is then used in each of the R, G and B components to generate the LDR color image. This color tone mapping correction approach, however, cannot be directly applied to most videos since they are usually encoded in the YCbCr color space. This paper addresses this problem and proposes a tone-mapping method that is applied directly on the YCbCr signals. Experimental results show that the Cb and the Cr signals generated by our method are almost identical to those produced with the conventional pipeline up to round-off errors, with average PSNR at about 55 dB and average SSIM at 0.991. By avoiding all the round-off errors introduced in the conventional method, our approach provides a more accurate LDR picture. Moreover, the proposed solution has significantly lower complexity because it bypasses the processes such as color space transformation and up-sampling which are required by the conventional method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".