Visual Comfort Amelioration Technique for Stereoscopic Images: Disparity Remapping to Mitigate Global and Local Discomfort Causes
Bibliographic record
Abstract
This paper proposes a new disparity remapping framework to improve the visual comfort of stereoscopic images. The proposed framework adaptively remaps disparities of a scene according to different causes of visual discomfort. A linear disparity remapping is first performed in order to address visual discomfort induced by excessive disparities. This linear remapping changes the disparities of the scene to obtain an overall target disparity range. Then, a nonlinear disparity remapping process selectively adjusts the disparity of problematic local disparity ranges according to their contribution to the visual discomfort. The proposed nonlinear disparity remapping process enables us to minimize the loss in perceived depth range while further improving visual comfort. The effectiveness of the proposed disparity remapping framework has been successfully evaluated by subjective assessments of visual comfort and naturalness. Experimental results demonstrate the validity of the proposed remapping framework. More importantly, we show that the nonlinear refinement of disparity in problematic regions can efficiently improve visual comfort while maintaining the naturalness of the scene.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".