Video Upscaling in Extreme Edge Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As digital media consumption skyrockets, there is an escalating demand for superior video streaming quality in settings with varied internet connectivity and restricted bandwidth. Addressing this challenge, our research introduces the Extreme Edge-enhanced Streaming (EEDES) scheme, capitalizing on the underutilized resources of edge devices like smartphones, laptops, and connected vehicles. This approach harnesses edge computing alongside machine learning to enhance low-bitrate video frames to higher resolutions. By segmenting video frames into smaller sub-frames, our method distributes the processing tasks across a network of available edge devices. This strategy employs a Super-resolution (SR) machine learning technique to execute tasks on these devices, maximizing their computational potential.
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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 | 0.001 |
| Open science | 0.000 | 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 it