A study on using spectral saliency detection approaches for image quality assessment
Bibliographic record
Abstract
Recent developments in the field of full reference image quality assessment (FR-IQA) have witnessed the use of spectral residual (SR) based index as a fast measure with high accuracy. Following SR, several variants of spectral measures for visual saliency have come up. These new measures differ in their computational times as well as in performances and have established themselves better than or competitive with SR as measures of visual saliency. The effectiveness of these measures in FR-IQA is still an open question. In this paper, a study to evaluate the performance of the recent spectral approaches for visual saliency (hence spectral saliency) for FR-IQA is presented. We have fixed a framework for FR-IQA to maintain uniformity in the evaluation process. Also, the parameters required by the framework are chosen to bring out the best potential of each measure. Our experiments on six benchmark databases reveal some insightful details about the usage of these measures to form an FR-IQA measure.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".