Deep Quality: A Deep No-reference Quality Assessment System
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
Image quality assessment (IQA) continues to garner great interestin the research community, particularly given the tremendousrise in consumer video capture and streaming. Despite significantresearch effort in IQA in the past few decades, the area of noreferenceimage quality assessment remains a great challenge andis largely unsolved. In this paper, we propose a novel no-referenceimage quality assessment system called Deep Quality, which leveragesthe power of deep learning to model the complex relationshipbetween visual content and the perceived quality. Deep Qualityconsists of a novel multi-scale deep convolutional neural network,trained to learn to assess image quality based on training samplesconsisting of different distortions and degradations such as blur,Gaussian noise, and compression artifacts. Preliminary results usingthe CSIQ benchmark image quality dataset showed that DeepQuality was able to achieve strong quality prediction performance(89% patch-level and 98% image-level prediction accuracy), beingable to achieve similar performance as full-reference IQA methods.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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