Green Video Compression for Portable and Low-Power Applications
Bibliographic record
Abstract
This chapter presents recent advances in the implementation of video compression that allow for energy efficiency and management. Algorithmic complexity reduction and complexity management are key to the implementation of video compression in a green computing environment. The authors concentrate on low-power applications (such as smartphones or autonomous cameras), in which the ability to manage algorithmic complexity (and thus energy consumption) to match battery conditions and the priority of other tasks is one of the key enablers for reduced energy consumption. First, a study of the state-of-the-art video coding standard H.264/AVC and an analysis of its encoder complexity will be presented. Secondly, low complexity H.264/AVC encoder implementations will be explored in two categories as: Low Complexity Motion Estimation Algorithms and Low Complexity Mode Decision Algorithms. Later, a discussion of Complexity Scalable Encoding Algorithms that can adaptively adjust their computational complexities will follow. During the discussions, the authors will also introduce a novel framework for managing the complexity of an H.264/AVC encoder in a processor or power constrained environment as well as a complexity reduction tool. The chapter will conclude with a discussion about the future of sustainable green computing in video compression, followed by summary and concluding remarks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".