Bibliographic record
Abstract
Perceptual image quality assessment (IQA) is an important research topic of visual signal processing both in its own right and for its utility in designing various optimal image processing and coding algorithms. This work is concerned with an issue that has been largely overlooked by the research community of IQA, that is, the monotonicity, or lack of it, between the subjective scores and the predictions of image quality metrics (IQM) for images with compression artifacts. We analyze the data of several well-known databases for IQA and expose among them a large number of instances of non-monotonicity between subjective and objective quality scores. Further, we observe that a nonlinear dynamical model of 3D cusp catastrophe can well explain the intricate relationship between the subjective and objective quality scores. Our findings identify an inherent flaw of current signal-distance or fidelity-based IQMs, which neglect the psycho-physiological aspect of human visual perception. This research suggests a new direction of IQA research and it also sheds light on the design of subjective quality evaluation process.
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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 | 0.008 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".