A Multiscale Objective Function for Camera Color Correction
Why this work is in the frame
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Bibliographic record
Abstract
Color correction (CC) plays a pivotal role in camera imaging. Existing approaches usually conduct CC tuning by minimizing ∆E (e.g. ∆E2000), a standard metric proposed by CIE for representing color differences in LAB space. However, we observe that not all the colors with identical ∆E error to the target color have with same perceptual preference. Consequently, optimizing CC by minimizing ∆E solely does not always produce satisfactory color-rendition accuracy. To deal with the problem, in this paper, we propose a new score function, namely Ψ, for a more accurate discrimination of different color-rendition mappings. This is achieved by a multi-scale objective incorporating not only ∆E, but also ∆H and ∆C, which respectively indicate color differences from hue and chroma perspectives. We describe the details of Ψ and show how to adjust its parameters for different preferences. We verify the usefulness of Ψ in experiments by embedding it in various CC tuning algorithms. The empirical results show that Ψ consistently leads to better color-rendition accuracy not only in training but also in validation sets. Finally, we deploy our new objective for tuning a real-world commercial digital camera and show that it delivers improved performance.
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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