An FFT-based visual quality metric robust to spatial shift
Bibliographic record
Abstract
In recent years, several metrics have been developed for measuring image visual quality, including the MSSIM and the visual information fidelity (VIF). However, these metrics are not robust to spatial shifts, meaning that when the reference and distorted images are misaligned by a few pixels, these metrics will produce very low scores, which is undesirable. In this paper, we extend the SSIM metric to make it robust to spatial shifts by first pre-processing the input images with the Fast Fourier transform (FFT). We then apply the magnitude of the transformed Fourier coefficients to the existing metrics because these coefficients are shift-invariant. Our assumption is that if we shift the image by a small amount of pixels, then it will not affect the perceived quality. Experimental results show that the proposed method is attractive for measuring the visual quality of 2D images as it is far less complex than the current approach, which consists in performing global motion estimation to align the input images prior to applying the metrics, and offers better accuracy.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".