An Entropy-Based Inverse Tone Mapping Operator with Improved Color Accuracy for High Dynamic Range Applications
Bibliographic record
Abstract
Conversion of existing image and video content to High Dynamic Range (HDR) using inverse Tone Mapping Operators (iTMOs) is expected to open new market opportunities for studios and content owners. One of the issues facing iTMOs arises from the fact that changing the input Standard Dynamic Range (SDR) luminance to HDR results in color shifts, regardless of the color space or gamut that the operation takes place, requiring the use of an efficient color restoration method. Therefore, maintaining the color accuracy between the input SDR and output HDR content is of high importance in designing iTMOs. In this paper, we propose an iTMO capable of maintaining the color accuracy between the original SDR and the resulting HDR output, while at the same time providing a good balance between the overall brightness and contrast in the HDR content. The results of our subjective evaluation demonstrate that the colors of the generated HDR content closely match those of the original SDR input, outperforming the existing state-of-the-art iTMOs and making the resulting HDR look natural and more appealing to the viewers.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".