Video communication systems with heterogeneous clients
Bibliographic record
Abstract
Modern wireless mobile devices have evolved to small computers that can render multimedia content, while desktop/laptop computers have become more computationally powerful with faster Internet access. As these computing devices getting more popular, users demand for more and higher quality videos in many communication applications, where clients are heterogeneous in terms of network bandwidth and computing power. The goal of this thesis is to improve client perceived-quality of various video communication systems by adopting scalable video coding tools that enable efficient rate adaptation. We seek to understand scalable coding standards and design optimization and streaming algorithms to make the best possible use of them in practical systems. We consider practical problems of video communication systems in three different environments: Internet streaming systems, TV broadcast networks, and mobile video communication systems. We propose efficient algorithms to solve the considered problems. We evaluate the proposed algorithms using numerical methods and/or simulations. Most importantly, we design and implement testbeds to validate our algorithms. The expected results of applying our algorithms to video communication systems are better video quality and higher user satisfaction as well as better bandwidth utilization and lower processing overhead.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".