Fully Connected Network for HEVC CU Split Decision equipped with Laplacian Transparent Composite Model
Why this work is in the frame
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Bibliographic record
Abstract
High Efficiency Video Coding (HEVC) improves rate distortion (RD) performance significantly, but at the same time is computationally expensive due to the adoption of a large variety of coding unit (CU) sizes in its RD optimization. In this paper, we investigate the application of fully connected neural networks (NNs) to this time-sensitive application to improve its time complexity, while controlling the resulting bitrate loss. Specifically, four NNs are introduced with one NN for each depth of the coding tree unit. These NNs either split the current CU or terminate the CU search algorithm. Because training of NNs is time-consuming and requires large training data, we further propose a novel training strategy in which offline training and online adaptation work together to overcome this limitation. Our features are extracted from original frames based on the Laplacian Transparent Composite Model (LPTCM). Experiments carried out on all-intra configuration for HEVC reveal that our method is among the best NN methods, with an average time saving of 38% and an average controlled bitrate loss of 1.6%, compared to original HEVC.
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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