Towards a Full-Reference Quality Assessment for Color Images Using Directional Statistics
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
This paper presents a novel computational model for quantifying the perceptual quality of color images consistently with subjective evaluations. The proposed full-reference color metric, namely, a directional statistics-based color similarity index, is designed to consistently perform well over commonly encountered chromatic and achromatic distortions. In order to accurately predict the visual quality of color images, we make use of local color descriptors extracted from three perceptual color channels: 1) hue; 2) chroma; and 3) lightness. In particular, directional statistical tools are employed to properly process hue data by considering their periodicities. Moreover, two weighting mechanisms are exploited to accurately combine locally measured comparison scores into a final score. Extensive experimentation performed on large-scale databases indicates that the proposed metric is effective across a wide range of chromatic and achromatic distortions, making it better suited for the evaluation and optimization of color image processing algorithms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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