Adaptive Watermarking and Tree Structure Based Image Quality Estimation
Bibliographic record
Abstract
Image quality evaluation is very important. In applications involving signal transmission, the Reduced- or No-Reference quality metrics are generally more practical than the Full-Reference metrics. In this study, we propose a quality estimation method based on a novel semi-fragile and adaptive watermarking scheme. The proposed scheme uses the embedded watermark to estimate the degradation of cover image under different distortions. The watermarking process is implemented in DWT domain of the cover image. The correlated DWT coefficients across the DWT subbands are categorized into Set Partitioning in Hierarchical Trees (SPIHT). Those SPHIT trees are further decomposed into a set of bitplanes. The watermark is embedded into the selected bitplanes of the selected DWT coefficients of the selected tree without causing significant fidelity loss to the cover image. The accuracy of the quality estimation is made to approach that of Full-Reference metrics by referring to an "Ideal Mapping Curve" computed a priori. The experimental results show that the proposed scheme can estimate image quality in terms of PSNR, wPSNR, JND and SSIM with high accuracy under JPEG compression, JPEG2000 compression, Gaussian low-pass filtering and Gaussian noise distortion. The results also show that the proposed scheme has good computational efficiency for practical applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".