Spatiotemporal H.264/AVC Video Adaptation with MPEG‐21
Bibliographic record
Abstract
The ubiquitous computing concept has brought a revolution permitting consumers to access multimedia contents anytime and in any multimedia capable device. Streaming rich media contents to small handheld devices like cell phone, PDA is a reality now due to the advancement in mobile data communication networks. To lessen the gap between the concept and reality, media adaptation is receiving attention to make the presence of the identical content in different devices viable. The Universal Multimedia Access (UMA) concept is a way to slam close this gap between consumers and providers by allowing access to multimedia contents from anywhere, anytime, and with any device. Video adaptation is a newly introduced practice for direct manipulation of an encoded bitstream to meet resource constraints without having to encode the video from scratch. For example, spatial adaptation is performed to match the original video to a device's screen resolution or varying network bandwidth by cropping desired frames to an area of interest from the original bitstream. This chapter describes compressed-domain patiotemporal adaptation for video contents using MPEG-21 generic Bitstream Syntax Description (gBSD). In addition, it also covers how this adaptation scheme can be used for online video adaptation in a Peer-to-Peer environment. © 2011 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".