Blind Image Quality Assessment: A Fuzzy Neural Network for Opinion Score Distribution Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Image quality assessment (IQA) has always been a popular research topic. There have been many methods proposed for predicting image quality, also known as the mean opinion score (MOS). However, it is worth noting that different people may assign different opinion scores to the same image. Image quality described by all subjective opinion scores can express rich subjective information about the image, such as diversity and uncertainty, which cannot be accurately described by a single MOS. Therefore, this paper proposes a fuzzy neural network to predict the opinion score distribution (OSD) of image quality. The fuzzy neural network includes three sub-networks: a feature extraction network, a feature fuzzification network, and a fuzzy learning network. First, a novel network is designed to extract image features. The extracted features are then fuzzified by fuzzy theory to model the epistemic uncertainty in the feature extraction process. Finally, the OSD of image quality is predicted using the fuzzy learning network by learning the mapping from fuzzy features to fuzzy uncertainty when rating image quality. In addition, to train the proposed fuzzy neural network, we employ a new loss function based on the quantile and the cumulative density function. We experimentally validate the feasibility and superiority of the proposed method in two aspects. On the one hand, we demonstrate the performance of the proposed method in predicting the OSD of image quality on the SJTU IQSD and KonIQ-10K databases. On the other hand, we also prove the feasibility of the proposed method in predicting the MOS of image quality on several popular IQA databases, including CSIQ, TID2013, LIVE MD, and LIVE Challenge.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it