High dynamic range image tone mapping by maximizing a structural fidelity measure
Bibliographic record
Abstract
Tone mapping operators (TMOs) that convert high dynamic range (HDR) images to standard low dynamic range (LDR) images are highly desirable for the visualization of these images on standard displays. Although many existing TMOs produce visually appealing images, it is until recently validated objective measures that can assess their quality have been proposed. Without such objective measures, the design of traditional TMOs can only be based on intuitive ideas, lacking clear goals for further improvement. In this paper, we propose a substantially different tone mapping approach, where instead of explicitly designing a new computational structure for TMO, we search in the space of images to find better quality images in terms of a recent objective measure that can assess the structural fidelity between two images of different dynamic ranges. Specifically, starting from any initial image, the proposed algorithm moves the image along the gradient ascent direction and stops until it converges to a maximal point. Our experiments show that the proposed algorithm reliably produces better quality images upon a number of state-of-the-art TMOs.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".