Buffer Management for 3D Image-based Rendering over Wireless Network with QoS Adaptation
Bibliographic record
Abstract
As an alternative way of rendering novel views of real and/or virtual environments to geometry-based models, 3D image-based rendering is getting more attention. Taking the weak hardware acceleration support of mobile devices into consideration, image-based rendering is a good choice for interactive 3D applications for such devices. However, because of the small storage size of mobile devices, the large size of the data set used by most modern image-based rendering techniques often introduces difficulties in utilizing image-based rendering on mobile devices. Therefore, a reference image buffer management mechanism is critical to the success of image-based rendering techniques on mobile devices. Different buffer management policies may result in different quality-of-service. In this paper, we compare the performance of several buffer management policies based on customized QoS factors for image-based rendering techniques. In order to make our experiments general, we show that image-based rendering techniques are interpolations of the Plenoptic function and define the QoS factors based on the interpolation theory. The results of the experiments show the impacts of different buffer management policies on customized QoS factors. Because the QoS factors do not depend on any special algorithms, this result keeps true for all modern image-based rendering algorithms
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".