AdaDSR: Adaptive Configuration Optimization for Neural Enhanced Video Analytics Streaming
Bibliographic record
Abstract
Neural-based super-resolution (SR) has achieved great success in enhancing image or video quality, creating new opportunities for building bandwidth-efficient and high-accuracy video analytics (VAs) systems. Intuitively, with the help of SR techniques, cameras only need to send downsampled low-quality frames to the server in a canonical edge-assisted VAs framework. The server-side SR model then upscales the quality of received frames for the subsequent VAs tasks, incurring thus substantially reduced bandwidth consumption. Nonetheless, as revealed by our measurement results on real-world video clips, higher delivery quality does not necessarily lead to higher analysis accuracy. This motivates us to study the content-adaptive downsampling and upscaling ratio selection problem for VAs streaming. We propose an SR-based VAs framework, named AdaDSR that can dynamically select the optimal downsampling and upscaling ratios so that the system utility can be maximized. AdaSDR is configured to balance the tradeoffs among accuracy, network cost, and computational cost. It further leverages the temporal consistency of videos to skip trivial decisions so that the camera’s processing overhead can be reduced. Experiments on real-world video data sets demonstrate that AdaDSR can improve the average utility by 7.2%–18.4% when compared with state-of-the-art approaches under diverse video scenes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".