SmartColor: Real-time color correction and contrast for optical see-through head-mounted displays
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
Users of optical see-through head-mounted displays (OHMD) perceive color as a blend of the display color and the background. Color-blending is a major usability challenge as it leads to loss of color encodings and poor text legibility. Color correction aims at mitigating color blending by producing an alternative color which, when blended with the background, more closely approaches the color originally intended. To date, approaches to color correction do not yield optimal results or do not work in real-time. This paper makes two contributions. First, we present QuickCorrection, a realtime color correction algorithm based on display profiles. We describe the algorithm, measure its accuracy and analyze two implementations for the OpenGL graphics pipeline. Second, we present SmartColor, a middleware for color management of userinterface components in OHMD. SmartColor uses color correction to provide three management strategies: correction, contrast, and show-up-on-contrast. Correction determines the alternate color which best preserves the original color. Contrast determines the color which best warranties text legibility while preserving as much of the original hue. Show-up-on-contrast makes a component visible when a related component does not have enough contrast to be legible. We describe the SmartColor's architecture and illustrate the color strategies for various types of display content.
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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