Artifacts Reduction GAN For Enhancing Quality Of Compressed Panoramic Video
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
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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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 | 0.001 |
| Open science | 0.000 | 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