SmartColor: Real-Time Color and Contrast Correction for Optical See-Through Head-Mounted Displays
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 approximates the color originally intended. In this paper we present an end-to-end approach to the color blending problem addressing the distortions introduced by the transparent material of the display efficiently and in real time. We also present a user evaluation of correction efficiency. Finally, we present a graphics library called SmartColor showcasing the use of color correction for different types of display content. 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 supports 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 SmartColor's architecture and illustrate the color strategies for various types of display content.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".