Deep Convolutional Feature-Driven Rate Control for the HEVC Encoders
Bibliographic record
Abstract
This work proposes a rate control model based on deep convolutional features to improve the video coding performance of the HEVC encoders under the random access (RA) configuration. The proposed algorithm extracts high-level features from the original and previous coded frames using a pretrained visual geometry group (VGG-16) model by considering characteristics of a different temporal layer for the RA configuration. Subsequently, R–λ parameters (alpha and beta), bit allocation, λ estimation, and quantization parameter decision at frame-level are formulated by utilizing the extracted high-level features to maintain video quality and bitrate accuracy control. In addition, bit allocation at the group-of-picture (GOP)-level rate control is proposed with perceptual-based thresholding to control smooth bitrates and visual quality between adjacent GOPs. The results verify that the proposed algorithm is efficient in coding performance and bit accuracy by keeping visual quality. Compared with the existing R–λ rate model in HM-16.20, the proposed models can achieve an average BD-rate gain of -4.39% and -8.74% in PSNR and MSSSIM metrics for the RA configuration, respectively.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".