A Lightweight Model for Deep Frame Prediction in Video Coding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent studies have demonstrated the efficacy of deep neural network (DNN)-based inter frame prediction for video coding. The network commonly used in these studies is built upon a U-Net-like architecture and produces content-adaptive 1-D separable filters with a large number of taps for frame prediction. This leads to a model with a large number of parameters. In this paper, we propose a lighter version of the network with significantly fewer parameters, by making use of dilated convolutional layers and making the U-Net shallower. In addition, we introduce a DCT-based ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -loss term that encourages compression, and explore several ways of integrating our lightweight model into HEVC. Both frame prediction accuracy and coding efficiency are compared against previous works. The experiments show that the proposed model achieves up to 6.4% average bit reduction in terms of BD-Bitrate against HEVC, which is significantly better than existing methods in the literature.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it