Optimized contrast reduction for crosstalk cancellation in 3D displays
Bibliographic record
Abstract
Subtractive crosstalk cancelation is an effective way to reduce the appearance of ghosting in 3D displays. However, effective cancelation requires the black level of the input images to be raised above zero, which reduces the image contrast and visual quality. Previous methods for selecting the raised black level do not consider the image content; they are either based on the worst case or they do not guarantee complete crosstalk cancelation. Previous methods also scale the red, green and blue channels independently, which results in images with washed out colors. This paper provides two contributions; first we derive the minimum amount that the black level has to be raised when using linear scaling in RGB space to ensure crosstalk can be fully cancelled out for a particular image. Second we propose that instead of scaling the images in RGB space, to scale the luma channel in YCbCr color space while keeping the chroma values constant to better preserve color. We also derive the minimum amount that the luma range has to be compressed to ensure that crosstalk can be fully canceled out. Experimental results show that our methods produce images with better color and contrast compared to scaling the RGB channels based on the worst case, while still guaranteeing crosstalk can be fully canceled out.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".