High Dynamic Range Image Compression by Optimizing Tone Mapped Image Quality Index
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
Tone mapping operators (TMOs) aim to compress high dynamic range (HDR) images to low dynamic range (LDR) ones so as to visualize HDR images on standard displays. Most existing TMOs were demonstrated on specific examples without being thoroughly evaluated using well-designed and subject-validated image quality assessment models. A recently proposed tone mapped image quality index (TMQI) made one of the first attempts on objective quality assessment of tone mapped images. Here, we propose a substantially different approach to design TMO. Instead of using any predefined systematic computational structure for tone mapping (such as analytic image transformations and/or explicit contrast/edge enhancement), we directly navigate in the space of all images, searching for the image that optimizes an improved TMQI. In particular, we first improve the two building blocks in TMQI—structural fidelity and statistical naturalness components—leading to a TMQI-II metric. We then propose an iterative algorithm that alternatively improves the structural fidelity and statistical naturalness of the resulting image. Numerical and subjective experiments demonstrate that the proposed algorithm consistently produces better quality tone mapped images even when the initial images of the iteration are created by the most competitive TMOs. Meanwhile, these results also validate the superiority of TMQI-II over TMQI.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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