An Investigation of Latency-Accuracy Trade-off in Inter-frame Video Prediction using Quantized CNNs
Bibliographic record
Abstract
Real-time video streaming has become the largest portion of internet traffic in recent years. Therefore, improving the efficiency of video coding remains an important research issue. Modern video codecs perform inter-frame prediction by motion estimation. However, inter-frame prediction is one of the most computationally expensive and time-consuming operations in video coding. Convolutional neural networks (CNN) have been used in recent research for inter-frame prediction tasks. The CNN architectures in previous work use floating point arithmetic whereas motion estimation in video codecs only use integer arithmetic. Thus, inter-frame prediction using CNNs instead of motion estimation may not always result in better time complexity. Floating point CNNs can be quantized into integer CNNs. Integer CNNs can reduce network latency but can also result in a loss of prediction accuracy. In this paper, we investigate the latency vs accuracy trade-off of quantized CNNs in inter-frame bi-prediction. We present experimental results which demonstrate that the integer CNN is at least 5% faster than the floating point CNN, while the prediction quality degradation of the integer CNN is no more than 0.6 dB in PSNR.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".