Image quality assessment based on multiple watermarking approach
Bibliographic record
Abstract
Automatic monitoring of image/video quality is very important in modern multimedia communication services. We are interested in digital watermarking as a promising approach to image quality assessment without reference to the original image. The proposed methodology makes use of wavelet-based embedding of multiple watermarks with robustness control in order to capture the degree of the degradation undergone by a received image. The watermark robustness is controlled through 1) embedding and detection of multiple watermarks, 2) multi-resolution and directional subband selection, 3) perceptual watermark weighting and 4) fine watermark strength adjustment process. At the receiver end, the detection or lack of detection of the watermarks in a received image are used to estimate image's PSNR range and determine its associated quality attribute. Simulation results show the efficiency of such watermarking scheme in assessing the quality level of test images under JPEG compression.
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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 | 0.001 | 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".