Perceptual evaluation of multi-exposure image fusion algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Multi-exposure image fusion is considered an effective and efficient quality enhancement technique widely adopted in consumer electronics products. Nevertheless, little work has been dedicated to the quality assessment of fused images created from natural images captured at multiple exposure levels. In this work, we first build a database that contains source input images with multiple exposure levels (≥ 3) together with fused images generated by both classical and state-of-the-art image fusion algorithms. We then carry out a subjective user study using a multi-stimulus scoring approach to evaluate and compare the quality of the fused images. Considerable agreement between human subjects has been observed. Our results also show that existing objective image quality models developed for image fusion applications either poorly or only moderately correlate with subjective opinions.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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