Reversible Data Hiding in Videos for Better Visibility and Minimal Transfer
Bibliographic record
Abstract
Performing data hiding in videos is very popular now. Video data hiding has large number of applications as it is more secure and also video has high frequency over the internet. When the amount of data to be embedded into the video increases it can adversely affect the quality of the video making it unsuitable for many applications in the area of defence, military, medical, satellite field etc. The important concerns in the area of data hiding in videos are its high visual quality, size of the video stream, the delay that occurs during the network transmission. In the case of MPEG or H.264 videos which are of great visual quality have their size high, so transmission of these videos can be a difficult task even though they are superior in visual quality. Due to the transmission delay, there arise practical problems in using these high quality videos. Among those the most serious issue that the area of data hiding in video face are its poor illumination. Our new method proposes a novel concept where data hiding and the high quality for poor illumination videos are given equal importance. In the proposed method, we are performing contrast enhancement, improving visual quality in the video streams. The noted point is that we are strictly preserving the video file size even after performing contrast enhancement in the videos. The result should always be of better visual quality then only it becomes practically useful.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".