A framework for MPEG-21 DIA based adaptation and perceptual encryption of H.264 video
Bibliographic record
Abstract
A ubiquitous computing concept permits end users to have access to multimedia and digital content anywhere, anytime and in any way they want. As a consequence, the importance of resource customization according to user preferences and device requirements set the primary challenge towards seamless access. Moreover, once a suitable customization approach has been decided (e.g. adaptation), deploying it in the existing network requires a generic and widely accepted standard applied to the process. With the advancement of time, performing encryption in the compressed domain should also be taken care of not only for serving sensitive digital contents but also for offering security as an embedded feature of the adaptation practice to ensure digital right management and confidentiality. In this paper, we present an architecture for temporal adaptation of ITU-T H.264 video conforming to ISO/IEC MPEG-21 DIA. In addition, we present a perceptual encryption scheme that is integrated in the system for video encryption. The framework enables video bitstreams to be adapted and encrypted in the compressed domain, eliminating cascaded adaptation (i.e. decoding - adaptation - encoding). The encryption framework is applied on the adapted video content, which reduces computational overhead compared to that on the original content. A prototype, based on the proposed architecture and experimental evaluations of the system as well as its performance supporting the architecture are also presented.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".