Image quality monitoring using spread spectrum watermarking
Bibliographic record
Abstract
An improved blind image quality assessment scheme that is based on the Watson's just noticeable difference (JND) modulated spread spectrum watermarking, is proposed. For the purpose of quality monitoring, a watermark is embedded into the original image. The image quality is estimated based on the detected watermark at the receiver side. In terms of the peak signal-to-noise ratio (PSNR), the proposed method is shown to be more robust and less perceptible than simple spread spectrum watermarking. This is due to several factors: an optimum detector is used for watermark detection, the watermark is appropriately selected from a set and the detector parameter is adjusted accordingly to closely yield an empirical ideal quality curve. The proposed method was tested by finding the quality estimates of different images compressed with different quality factors. The results indicate that the method can accurately estimate the quality of the received images based on the detected watermark power.
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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".