Psychovisual correlations with multifractal measures for wavelet and wavelet packet progressive image transmission
Bibliographic record
Abstract
In an effort to develop a quantitative measure for image quality, this paper looks for psychovisual correlations of image quality to multifractal measures. Image metrics such as peak signal-to-noise ratio are not well suited as perceptual indicators and other techniques are primarily limited to just noticeable differences which limit their use in general. It is desirable to have image quality metrics for progressive image transmission that are more general and can evaluate images produced during a progressive image transmission, from the worst image reconstruction step all the way to the final perfect image. We focus on how the Renyi (1955) dimension spectrum changes as more wavelet and wavelet packet coefficients are included in a progressive image transmission. Mean opinion score (MOS) results serve as an additional basis for analyzing how the progressive image transmission affect the Renyi dimension spectrum. While this paper does not give a final answer to what the best metric would be for image quality in a progressive image transmission, it does attempt to link perceptual quality with multifractal measures.
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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 | 0.000 | 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".