A Generative Adversarial Network Based Tone Mapping Operator for 4K HDR Images
Bibliographic record
Abstract
High dynamic range (HDR) has arguably been established as the preferred image and video format for content providers. As standard dynamic range (SDR) displays still dominate the market, there is a need for finding efficient ways to convert HDR content to the SDR format, a process known as tone mapping. Recently, many tone mapping operators (TMOs) have been proposed that are based on deep learning approaches. However, the biggest challenge in training such deep learning networks is lack of truthful SDR and HDR datasets that would lead to highly accurate TMOs. In this paper, we introduce a new high-quality 4K HDR-SDR dataset of image pairs, covering a wide range of brightness levels and colors. We propose a TMO that is based on the generative adversarial network architecture. Evaluation results showed that our method achieves high perceptual quality, maintaining artistic intent and providing better color representation compared to existing state-of-the-art TMOs. Data and code are available at: https://github.com/zjbthomas/TMO-GAN.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".