Panoramic Image Quality-Enhancement by Fusing Neural Textures of the Adaptive Initial Viewport
Why this work is in the frame
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Bibliographic record
Abstract
With the development of virtual reality (VR) technology, panoramic image has been widely applied in our life. Due to its large size, the existing streaming methods prefer only transmitting contents corresponding to the audience’s current viewport in high quality. This viewport-based transmission, however, suffers from severe delay as the viewport changes. In this paper, to fill in the time gap between the switch of viewport and the arrival of high-resolution content, we introduce an end-to-end network at the receiver. The main idea is to use the neural textures in the adaptive initial viewport to improve the quality of regions around it. When the viewport changes but the high-resolution content has not arrived, the image enhanced by our strategy is acceptable to satisfy visual experience as the experiment results show. To the best of our knowledge, this is the first panoramic image quality-enhancement method considering content continuities and internal features.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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