Image Quality Score Distribution Prediction via Alpha Stable Model
Bibliographic record
Abstract
Based on potentially subjective and diverse image quality scores given by a group of subjects, we propose to predict the distribution of image quality scores rather than the mean opinion score (MOS) of image quality. Therefore, in this paper, we use an alpha stable model to parameterize the image quality score distribution (IQSD), and propose an objective method to predict the alpha-stable-model-based IQSD. First, the LIVE database is re-recorded. Specifically, we invite a large group of subjects (187 valid subjects) to evaluate the quality of all 808 images in the LIVE database, with their scores forming reliable IQSDs. All images in the LIVE database and their collected subjective quality scores form a new image quality assessment database, named the SJTU IQSD database. We then propose a framework and algorithm to predict the alpha-stable-model-based IQSD, in which quality features are extracted from the structural and natural statistical information of each image, and support vector regressors are trained to predict the alpha stable model parameters. Experiments carried out on the SJTU IQSD database verify the feasibility of using the alpha stable model to describe the IQSD, and the experimental results show that the alpha-stable-model-based IQSD can reflect a large amount of subjective information on image quality. We also prove that the objective alpha-stable-model-based IQSD prediction method is effective. The code and the SJTU IQSD database can be downloaded at ‘https://github.com/YixuanGao98/Image-Quality-Score-Distribution-Prediction-via-Alpha-Stable-Model.git’.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".