A Fully Automatic Content Adaptive Inverse Tone Mapping Operator With Improved Color Accuracy
Bibliographic record
Abstract
High Dynamic Range (HDR) technology offers a higher visual quality compared to its Standard Dynamic Range (SDR) counterpart, as it tries to imitate the way our eyes perceive brightness and color information. Converting SDR content to HDR format - using inverse Tone Mapping Operators (iTMOs) - to take advantage of the superior visual quality offered by HDR displays, is an attractive proposition to SDR content owners and real-time broadcasters. In this paper, we propose a novel content adaptive iTMO that works in the perceptual domain to model the sensitivity of the human eye to brightness changes in different areas of a scene. To preserve the overall visual impression, our proposed iTMO utilizes an entropy-based brightness segmentation, which also makes our method content adaptive. In addition, we propose a novel perception-based color adjustment method that can maintain the color accuracy between input SDR and generated HDR frames. By performing the color adjustment in the perceptual domain, our iTMO prevents hue shifts and generates HDR colors that closely follow their SDR counterparts. Our subjective evaluations indicate that our proposed method outperforms other state-of-the-art methods by an average of 81% in terms of visual quality, and 76% in terms of how closely the HDR colors match their SDR counterparts. In addition to subjective evaluations, we also performed objective evaluations using the HDR-VDP 2.2 and PU-SSIM metrics and concluded that, on average, our proposed iTMO outperforms the state-of-the-art methods in terms of these two metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".