Depth Personalization and Streaming of Stereoscopic Sports Videos
Bibliographic record
Abstract
Current three-dimensional displays cannot fully reproduce all depth cues used by a human observer in the real world. Instead, they create only an illusion of looking at a three-dimensional scene. This leads to a number of challenges during the content creation process. To assure correct depth reproduction and visual comfort, either the acquisition setup has to be carefully controlled or additional postprocessing techniques have to be applied. Furthermore, these manipulations need to account for a particular setup that is used to present the content, for example, viewing distance or screen size. This creates additional challenges in the context of personal use when stereoscopic content is shown on TV sets, desktop monitors, or mobile devices. We address this problem by presenting a new system for streaming stereoscopic content. Its key feature is a computationally efficient depth adjustment technique which can automatically optimize viewing experience for videos of field sports such as soccer, football, and tennis. Additionally, the method enables depth personalization to allow users to adjust the amount of depth according to their preferences. Our stereoscopic video streaming system was implemented, deployed, and tested with real users.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".