MarkWhite: An Improved Interactive White-Balance Method for Smartphone Cameras
Why this work is in the frame
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Bibliographic record
Abstract
White balance is an essential step for camera colour processing. The goal is to correct the colour cast caused by scene illumination in captured images. In this paper, three user-interactive white balance methods for smartphone cameras are implemented and evaluated. Two methods are commonly used in smartphone cameras: predefined illuminants and temperature slider. The third method, called MarkWhite, is newly introduced into smartphone camera apps. Two user studies evaluated the accuracy and task completion time of MarkWhite and compared it to the existing methods. The first user study revealed that a basic version of MarkWhite is more accurate, slightly faster, and slightly more preferred over the two existing methods. The main user study focused on the full version of MarkWhite, revealing that it is even more accurate than the basic version and better than state-of-the-art industrial white balance methods on the latest smartphone cameras. The collective findings show that MarkWhite is a more accurate and efficient user-interactive white balance method for smartphone cameras, and more preferred by users as well.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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