Objective assessment of tone mapping algorithms
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
There has been a growing interest in recent years to develop tone mapping algorithms that can convert high dynamic range (HDR) to low dynamic range (LDR) images, so that they can be visualized on standard displays. With a number of tone mapping algorithms proposed, a natural question is which one gives the best performance. Although subjective assessment methods provide useful references, they are expensive and time-consuming, and are difficult to be embedded into the design stage of tone mapping algorithms for optimization and parameter tuning purposes. This paper focuses on objective assessment of tone mapping operators. Inspired by the success of the structural similarity index method for image quality assessment, we propose a new objective assessment algorithm that creates multi-scale similarity maps between HDR and LDR images. Our experiments show that the proposed method correlates well with subjective rankings of existing tone mapping operators. Furthermore, we demonstrate how the proposed algorithm can be employed in an existing tone mapping algorithm for optimal parameter tuning.
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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 | 0.000 | 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 | 0.000 |
| 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 it