Reliable 3D video streaming considering region of interest
Bibliographic record
Abstract
3D video applications are growing more common as communication technology becomes more predominant nowadays. With such increasing demand for the 3D multimedia services in either the wired or wireless networks, robust methods of video streaming will be introduced to show more favorable efficiency outcomes since packet failure is an integral characteristic of communication networks. This paper aims to introduce a new reliable method of stereoscopic video streaming based on multiple description coding (MDC) strategy. The proposed multiple description coding generates four 3D video descriptions considering the interesting objects contained in the scene. To be able to find the interesting objects in the scene, we use two metrics from the second-order statistics of the depth map image in a block-wise manner. Having detected the objects, the proposed multiple description coding algorithm generates the descriptions for the color video using a nonidentical decimation method with respect to the identified objects. To show how much reliable the proposed MDC method is, this article assumes that due to the unreliable communication channel, only one description, among four encoded descriptions, is delivered to the receiver successfully. Therefore, the receiver needs to estimate the missed descriptions' data from the available description. Since the human eye is more sensitive to objects than it is to pixels, the proposed method provides a better visual performance in view of its subjective assessment. Although, the objective test results verify the fact that the proposed method provides an improved performance than the Polyphase SubSampling (PSS) multiple description coding and our previous work using pixel variation. Regarding the depth map image, the proposed method generates the multiple descriptions according to the pixel prediction difficulty level. The considerable improvement achieved by the proposed method is shown with the peak signal-to-noise ratio (PSNR) and Structural SIMilarity (SSIM) simulation result.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".