Online-Learning-Based Complexity Reduction Scheme for 3D-HEVC
Bibliographic record
Abstract
3-D High Efficiency Video Coding (HEVC) is a new emerging video compression standard for multiview video applications. This standard utilizes advanced interview prediction characteristics in addition to the prediction features of the HEVC standard for efficient encoding of multiview video content. While using combined features improves the compression performance by utilizing the correlation between the views captured from slightly different angles of the same scene, they also increase coding complexity. The focus of this paper is on developing an efficient complexity reduction scheme for 3D-HEVC, with the intention to facilitate the adoption of this upcoming standard, especially for real-time applications. In this regard, first, we introduce two ways to decrease the complexity of the inter-/ intra-mode search process of the to-be-encoded blocks in the dependent texture views (${\mathrm {DV}}_{t}\text{s}$ ) of 3D-HEVC. Then, we propose a hybrid complexity reduction scheme that utilizes the two-mode prediction approaches, motion information of the base texture view (BVt), and the rate distortion cost of the already encoded blocks in the BVt and DVt. The performance of our proposed scheme is tested for the case with two views (i.e., base view + dependent view). The evaluations confirm that our proposed hybrid complexity reduction scheme reduces the 3D-HEVC codec complexity by 67.70% on average for the DVt compared with the unmodified 3D-HEVC encoder, while maintaining the overall video quality. Compared with the state-of-the-art method, it reduces complexity by 25.74% on average.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".