3D Video Spatiotemporal Multiple Description Coding Considering Region of Interest
Bibliographic record
Abstract
3D video applications are being more favourable for observers as their requirements to receive and display 3D videos become more available recently. Therefore the demand for more processing power and bandwidth is increasing to stream and display 3D multimedia services in either the wired or wireless networks. Since channel failure has been always as an integral part of communication between a receiver and transmitters, a robust method of video streaming is always a hot topic for researchers. To make robustness against failure more stronger, it needs to increase redundancies however it destroys the coding and compressing efficiency. Therefore there is a trade-off problem between the coding efficiency and robustness of the stream. Among different methods of reliable video streaming, this paper introduces a new reliable 3D video streaming using hybrid multiple description coding. The proposed multiple description coding creates 3D video descriptions identifying interesting objects of the scene. To this end, a map for the region of interest is extracted from the depth map image first with a not complex algorithm compared to the available machine learning algorithm. Having realized region of interest, the proposed hybrid multiple description coding algorithm creates the descriptions for the color video using the advantages of both spatial and temporal multiple description coding methods; To this end, a non-identical decimation method concerning the identified objects assigns more bandwidth to those objects; second, background quality is improved with the temporal information. This way, first, the proposed method provides better visual performance as the human eye is more sensitive to objects than it is to pixels; second, the background is reconstructed with higher quality as it usually has a low movement and temporal information is a better choice to estimate the lost information. The objective test results verify the fact that the proposed method provides an improved performance than previous methods.
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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 | 0.001 | 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 | 0.000 |
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".