Artifacts Reduction GAN For Enhancing Quality Of Compressed Panoramic Video
Bibliographic record
Abstract
Panoramic video has the characteristics of high resolution, massive information and high sense of immersion, bringing unprecedented visual sensory enjoyment to us. However, considering the limited capacity of current cellular network, videos transmitted to users are often encoded, e.g. by High Efficiency Video Coding (HEVC), resulting in block artifact problems that significantly affects the visual quality. Thus, it is necessary to enhance the quality of compressed panoramic videos. Inspired by the convolutional networks (CNN) and generative adversarial networks (GAN), the paper proposes a deep GAN model -Artifacts Reduction GAN (ARGAN) which is able to enhance the quality of compressed panoramic videos. ARGAN has the ability of reducing artifacts caused by HEVC. Meanwhile, it can increase the visual realistic of the enhanced videos. We tested the performance of our model under PSNR, SSIM and Perception Index. Qualitative results are also provided to display the visual effects of ARGAN. Experimental results show that our method is superior to other quality enhancement methods in both qualitative and quantitative aspects.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".