Just-Noticeable-Difference Based Coding and Rate Control of Mobile 360° Video Streaming
Bibliographic record
Abstract
In recent years, 360ovideos have gained higher and higher popularity. Nonetheless, compared to two dimensional videos, the large-scale data volume renders it a bottleneck to deliver 360ocontent with constrained bandwidth resources. In this paper, we investigate user viewing behavior when they explore in immersive environment and propose a novel 360ojust-noticeable-difference (JND) model to characterize user's tolerance to visual distortion. In order to maximize user's quality of experience (QoE), we present a scheme, named JND-Based Streaming (JBS), to jointly optimize 360o video coding and streaming over mobile devices. Specifically, tiled 360ovideos are firstly encoded with the proposed JND model to reduce video file size. Then, a quality-driven streaming approach is designed to instruct tile-level bitrate allocation, considering subjective sensation. Thanks to the video file size reduction, tiles can be delivered with higher quality, which provides users with improved QoE. Experimental results based on real-world network traces demonstrate that, on average, JBS outperforms its counterparts by 12% and 57% in terms of perceived quality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".