Decoder-Complexity-Aware Encoding of Motion Compensation for Multiple Heterogeneous Receivers
Bibliographic record
Abstract
For mobile multimedia systems, advances in battery technology have been much slower than those in memory, graphics, and processing power, making power consumption a major concern in mobile systems. The computational complexity of video codecs, which consists of CPU operations and memory accesses, is one of the main factors affecting power consumption. In this article, we propose a method that achieves near-optimal video quality while respecting user-defined bounds on the complexity needed to decode a video. We specifically focus on the motion compensation process, including motion vector prediction and interpolation, because it is the single largest component of computation-based power consumption. We start by formulating a scenario with a single receiver as a rate-distortion optimization problem and we develop an efficient decoder-complexity-aware video encoding method to solve it. Then we extend our approach to handle multiple heterogeneous receivers, each with a different complexity requirement. We test our method experimentally using the H.264 standard for the single receiver scenario and the H.264 SVC extension for the multiple receiver scenario. Our experimental results show that our method can achieve up to 97% of the optimal solution value in the single receiver scenario, and an average of 97% of the optimal solution value in the multiple receiver scenario. Furthermore, our tests with actual power measurements show a power saving of up to 23% at the decoder when the complexity threshold is halved in the encoder.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".