Encoding and communication energy consumption trade-off in H.264/AVC based video sensor network
Bibliographic record
Abstract
Video sensor networks (VSN) offer an interesting platform for a distributed and flexible surveillance system. In such a system, video compression and wireless transmission are the major operations on each video node. For a battery-powered wireless video sensor, it is essential to maximize the power efficiency of these two operations. Currently, H.264/AVC is the most widely used ITU-T and ISO/IEC video coding standard. Previous works on determining the trade-off between compression and transmission that minimizes energy consumption consider oversimplified coding configurations, thus not taking full advantage of the flexibility and advanced features of H.264/AVC. Choosing the right configuration and setting parameters that lead to optimal encoding performance is of prime importance for video sensor network (VSN) applications, especially since VSN is constrained in terms of bandwidth and energy resources. This paper studies the relationship between the picture quality, the transmission rate, and the complexity of the encoder to expound the energy consumption trade-off between encoding and transmission in VSN. The results of our study can be used as guidelines in optimizing the overall power consumption of a VSN system as it detailed in the paper.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".