Increasing compression of JPEG images using steganography
Bibliographic record
Abstract
In this paper, we focus on the problem of improving image compression through steganography. Even if the purposes of digital steganography and data compression are by definition contradictory, these techniques might be used jointly to compress and to hide information within the same digital support. A novel image compression scheme, employing steganography to decrease the data file size, is investigated. That is, data compression is performed twice under this point of view. Using at first, the conventional standard JPEG which reduces redundant data, taking advantage of the energy compaction property, and secondly, by means of steganography which embeds some bits-blocks within its subsequent blocks of the same image. The embedded bits do not increase the file size of the compressed image, but as they are taken from and hidden within the image itself, the file size will be further decreased. Experimental results show that this promising technique has a wide potential in image coding.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".