Metric of image quality based on structural similarity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A new evaluation method of image quality based on its construction simulation is proposed to solve the limit of evaluation of perceptual distortion by analyzing the current characteristic of image quality evaluation method. Whole similarity obtained from luminance, contrast and image construction is the objective evaluation standard of image quality. The method fully considers the characteristic of structure information of image and vision of people, starts from the comprehension function of image context, and sets up the structure simulation computing model to evaluate the subjective perception to image quality. By theory deducing and algorithm validation, the evidences for selecting the image compressed algorithm and evaluating image quality are obtained. Reconstructed image after encoding by compression algorithm SPIHT (Set Partitioning in Hierarchical Trees) is compared with the traditional evaluation image based on Peak Signal-to-noise Ratio (PSNR), and experiment shows that the method proposed in the paper is a more effective evaluating method for image quality.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it