Perceptual noise shaping in dual-tree complex wavelet transform for image coding
Bibliographic record
Abstract
In this paper, we extend the idea of noise shaping for dual-tree complex wavelet transform (DCWT) image coding to a perceptual-based noise shaping. In classical noise shaping, the spatial error information is compensated by adding it back into the whole DCWT domain, which allows the retained to coefficients have better capability to approximate the original image. The proposed perceptual noise shaping introduces a perceptual weight to the spatial errors. The weight involves the structural similarity (SSIM) measurement and other adjustment parameters to shape the spatial errors. Experimental results show that the perceptual noise shaping has better results for visual quality and provides higher SSIM index than classical noise shaping. For example, the proposed perceptual noise shaping achieves an overall SSIM of 0.891 for the 8 bit 512×512 “barbara” compared to 0.878 in classical noise shaping when 5000 coefficients are retained.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".