A Skill-Based Visual Attention Model for Cloud Gaming
Bibliographic record
Abstract
Despite its recent advances and increasing industrial interest, cloud gaming’s high bandwidth usage is still one of its major challenges. In this paper, we demonstrate how incorporating visual attention into cloud gaming helps to reduce bitrate without negatively affecting the player’s quality of experience. We show that current visual attention models, which work well for normal videos, underperform in the context of cloud gaming videos. Hence, we propose our novel model, by developing a skill-based visual attention model, based on a cloud gaming dataset. First, it is demonstrated how players’ attention maps are correlated with their skill levels and how this can be exploited to improve the accuracy of visual attention modeling. Then, this fact is used to cluster attention maps, according to the player’s skill level. A simple yet effective method is introduced to predict players’ skill levels using their performance in game. Finally, the models are incorporated into the video encoder to perceptually optimize the bitrate allocation. Incorporating the player’s skill level into our model improves the accuracy of saliency maps by 14% with respect to the baseline, and 24% with respect to competing methods, in terms of Normalized Scanpath Saliency (NSS). Furthermore, we show that the maximum possible amount of video bitrate reduction depends on the player’s skill level. Experimental results show 13%, 5%, and 15% reduction in video bitrate for beginner, intermediate, and expert players, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".