A Binary Relation Fuzzy Soft Matrix-Theoretic Approach to Image Quality Measurement: Comparison with Statistical Similarity Metrics
Bibliographic record
Abstract
Image similarity assessment is a fundamental aspect of real-world applications and plays a crucial role in image processing.The structural similarity index (SSIM), which relies on statistical properties between two digital images, has been widely adopted.However, it struggles to detect or measure similarity at low peak signal-to-noise ratios (PSNR).In this study, a novel approach to image similarity for evaluating gray image quality is presented, termed Binary Relation Fuzzy Soft Matrix Image Similarity Measure (BR-FS-ISM).This method utilizes a fuzzy soft matrix-theoretic technique based on a new binary relation fuzzy soft matrix.The proposed BR-FS-ISM approach was tested under Gaussian noise conditions.Simulation results demonstrate that the novel BR-FS-ISM method outperforms the well-established SSIM metric, exhibiting the ability to detect and measure similarity at very low PSNR levels, with an average difference of approximately 10 dB.This paper suggests that the BR-FS-ISM approach offers a promising alternative to conventional statistical similarity measures for image quality assessment.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".