A Feature-Based Quality Metric for Tone Mapped Images
Bibliographic record
Abstract
With the development of high-dynamic-range images and tone mapping operators comes a need for image quality evaluation of tone mapped images. However, because of the significant difference in dynamic range between high-dynamic-range images and tone mapped images, conventional image quality assessment algorithms that predict distortion based on the magnitude of intensity or normalized contrast are not suitable for this task. In this article, we present a feature-based quality metric for tone mapped images that predicts the perceived quality by measuring the distortion in important image features that affect quality judgment. Our metric utilizes multi-exposed virtual photographs taken from the original high-dynamic-range images to bridge the gap between dynamic ranges in image feature analysis. By combining measures for brightness distortion, visual saliency distortion, and detail distortion in light and dark areas, the metric measures the overall perceptual distortion and assigns a score to a tone mapped image. Experiments on a subject-rated database indicate that the proposed metric is more consistent with subjective evaluation results than alternative approaches.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".