A new methodology to derive objective quality assessment metrics for scalable multiview 3D video coding
Bibliographic record
Abstract
With the growing demand for 3D video, efforts are underway to incorporate it in the next generation of broadcast and streaming applications and standards. 3D video is currently available in games, entertainment, education, security, and surveillance applications. A typical scenario for multiview 3D consists of several 3D video sequences captured simultaneously from the same scene with the help of multiple cameras from different positions and through different angles. Multiview video coding provides a compact representation of these multiple views by exploiting the large amount of inter-view statistical dependencies. One of the major challenges in this field is how to transmit the large amount of data of a multiview sequence over error prone channels to heterogeneous mobile devices with different bandwidth, resolution, and processing/battery power, while maintaining a high visual quality. Scalable Multiview 3D Video Coding (SMVC) is one of the methods to address this challenge; however, the evaluation of the overall visual quality of the resulting scaled-down video requires a new objective perceptual quality measure specifically designed for scalable multiview 3D video. Although several subjective and objective quality assessment methods have been proposed for multiview 3D sequences, no comparable attempt has been made for quality assessment of scalable multiview 3D video. In this article, we propose a new methodology to build suitable objective quality assessment metrics for different scalable modalities in multiview 3D video. Our proposed methodology considers the importance of each layer and its content as a quality of experience factor in the overall quality. Furthermore, in addition to the quality of each layer, the concept of disparity between layers (inter-layer disparity) and disparity between the units of each layer (intra-layer disparity) is considered as an effective feature to evaluate overall perceived quality more accurately. Simulation results indicate that by using this methodology, more efficient objective quality assessment metrics can be introduced for each multiview 3D video scalable modalities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".