Recurrent Neural Network-Based Video Compression
Bibliographic record
Abstract
Recently, video compression gained a large focus among computer vision problems in media technologies. Using state of the art video compression methods, videos can be transmitted in a better quality requiring less bandwidth and memory. The advent of neural network-based video compression methods remarkably promoted video coding performance. In this paper, a video compression method is presented based on Recurrent Neural Network (RNN). The method includes an encoder, a middle module, and a decoder. Binarizer is utilized in the middle module to achieve better quantization performance. In encoder and decoder modules, long short-term memory (LSTM) units are used to keep the valuable information and eliminate unnecessary ones to iteratively reduce the quality loss of reconstructed video. This method reduces the complexity of neural network-based compression schemes and encodes the videos with less quality loss. The proposed method is evaluated using peak signal-to-noise ratio (PSNR), video multimethod assessment fusion (VMAF), and structural similarity index measure (SSIM) quality metrics. The proposed method is applied to two different public video compression datasets and the results show that the method outperforms existing standard video encoding schemes such as H.264 and H.265.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".