Comfort Evaluation of 3D Movies Based on Parallax and Motion
Bibliographic record
Abstract
The intensity of feeling and comfort are two contrasting aspects of 3D movies. This paper studies 3D comfort according to crosstalk, basic parallax, space motion, and depth of field based on pixels. A method for calculating 3D motion based on the XYZ axes is presented, and a pixel-based evaluation model of 3D comfort is established with parallax, motion, and other indexes. In order to support the production and evaluation process of 3D films, a 3D comfort evaluation tool is designed according to time series. As demonstrated experimentally, basic parallax, depth of field, and average crosstalk have certain impact on the entire 3D comfort, while space motion has a relatively salient effect. When applying our method, a steady control on 3D comfort is found in high-quality 3D films: About 92% of comfort is within the range of 1-3.5 points for Transformers: Age of Extinction (USA), whereas about 74% of comfort is within the range of 1-3.5 points for The Monkey King (China). Large depth of field for slow moving lens and small depth of field for fast moving lens were used in Avatar and Transformers, and these 3D art rules are not followed closely in The Monkey King. As a basic tool for 3D film production, our proposed method can be applied in creating, shooting, producing, and monitoring for a 3D film.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".