Bibliographic record
Abstract
The continuous developments and improvements of network infrastructure along with the growing number of modern smartphones, tablets and smart TVs have led to an increasing popularity of multimedia applications, such as video conferencing, video streaming and mobile TV. Towards delivering video stream to diverse devices over a heterogamous network, scalable video coding (SVC) has received many research attentions. SVC is an extension of H.264/AVC that allows a video streaming service provider to encode a high quality video into a number of scalable layers. The receivers of the stream may decode the video at the appropriate quality level that is suitable for their hardware/software capabilities and network connections. Nevertheless, compared to single layer H.264/AVC, the de facto standard for many commercial streaming service providers such as YouTube, SVC is not widely deployed, mostly due to its overhead in terms of bit rate and complexity. In this paper, we conduct a thorough study on the performance of SVC for full HD video streaming. Our performance analysis identifies good and bad uses of SVC, quantifies the coding overhead, and benchmarks the SVC video quality under different spatial, temporal, and quality settings. Through the use a set of carefully selected and diverse video sequences, we also identify the types of video that can benefit from SVC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".