Towards anovel perceptual color difference metric using circular processing of hue components
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a novel metric for image difference prediction, capable of handling color data. The proposed metric, namely, color difference index based on circular hue, is a full-reference based scheme, which independently processes achromatic and chromatic differences of two input color images. Within the framework, chromatic information is analyzed using two perceptual attributes, hue and chroma information, simulating human visual system mechanism. Unlike conventional approaches where the periodic nature of hue is disregarded, we propose to estimate hue difference by adopting theory of circular statistics. Performance of the proposed solution is validated using benchmark image quality assessment databases. Experimental results indicate the effectiveness of the proposed metric against a wide range of distortions, especially on chromatic distortions, making it better suited for color gamut mapping applications.
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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) | 0.000 | 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