A First Look at Open-GoP Streaming with Av1 S-Frames
Bibliographic record
Abstract
Recent improvements in adaptive video streaming have been significant in the areas of adaptation algorithms and encoding (e.g., emergence of AV1 codec). A promising but yet unexplored feature of AV1 is the switching frames (S-frames). In this paper, we explore the pros and cons of S-frames in DASH streaming and demonstrate that S-frames can improve the compression and quality switching capabilities of AV1. We conduct a measurement study to understand the extent of benefits of S-frames and find that 10% – 50% data savings are achievable for many encoding configurations where S-frame use is maximized. We further propose a stream structure where the number of S-frames used balances QoE improvement with the ability to play or seek randomly within the video in challenging network conditions. We evaluate the streams using S-frames in a DASH player under a variety of conditions. We find that when S-frame use and switching opportunities are both maximized, they significantly improve stall performance, the critical aspect of the Quality of Experience (QoE), by reducing stall time by at least 88 % in realistic bandwidth conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| 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 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".