Joint Encoding and Enhancement for Low-Light Video Analytics in Mobile Edge Networks
Bibliographic record
Abstract
In this paper, we present our design and analysis of a Joint Encoding and Enhancement (JEE) system for low-light video analytics in mobile edge networks. First, it is observed that, relying solely on a single pipeline for encoding and enhancement of mobile videos proves insufficient, because of the fluctuations in end-edge bandwidth and computing resources. Therefore, two distinct pipelines are introduced in the JEE system, namely, the encode-decode-enhance pipeline and the enhance-encode-decode pipeline. We then characterize the relationship of accuracy, transmission overhead, and computing overhead of these two pipelines through extensive experiments. Considering the significant demands of transmission and computing for low-light videos, we formulate an optimization problem to strike a balance between accuracy and delay, where the available end-edge bandwidth and computing resources are unknown in advance. To solve this mixed-integer nonlinear programming problem, we propose an algorithm based on online gradient descent, enabling adaptive pipeline selection and joint encoding and enhancement configuration. Theoretical analysis indicates that the proposed algorithm achieves sub-linear dynamic regret, highlighting its capability to the accuracy improvement and delay reduction in online environments. Experimental comparison against baselines demonstrates that, JEE can achieve up to a 27.32% increase in accuracy and a 26.18% reduction in delay.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".