MarkWhite: An Improved Interactive White-Balance Method for Smartphone Cameras
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".