Image quality of up-converted 2D video from frame-compatible 3D video
Bibliographic record
Abstract
In the stereoscopic frame-compatible format, the separate high-definition left and high-definition right views are reduced in resolution and packed to fit within the same video frame as a conventional two-dimensional high-definition signal. This format has been suggested for 3DTV since it does not require additional transmission bandwidth and entails only small changes to the existing broadcasting infrastructure. In some instances, the frame-compatible format might be used to deliver both 2D and 3D services, e.g., for over-the-air television services. In those cases, the video quality of the 2D service is bound to decrease since the 2D signal will have to be generated by up-converting one of the two views. In this study, we investigated such loss by measuring the perceptual image quality of 1080i and 720p up-converted video as compared to that of full resolution original 2D video. The video was encoded with either a MPEG-2 or a H.264/AVC codec at different bit rates and presented for viewing with either no polarized glasses (2D viewing mode) or with polarized glasses (3D viewing mode). The results confirmed a loss of video quality of the 2D video up-converted material. The loss due to the sampling processes inherent to the frame-compatible format was rather small for both 1080i and 720p video formats; the loss became more substantial with encoding, particularly for MPEG-2 encoding. The 3D viewing mode provided higher quality ratings, possibly because the visibility of the degradations was reduced.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".