Papers › Shift-tolerant Perceptual Similarity Metric
Shift-tolerant Perceptual Similarity Metric
Abhijay Ghildyal, Feng Liu
Existing perceptual similarity metrics assume an image and its reference are well aligned. As a result, these metrics are often sensitive to a small alignment error that is imperceptible to the human eyes. This paper studies the effect of small misalignment, specifically a small shift between the input and reference image, on existing metrics, and accordingly develops a shift-tolerant similarity metric. This paper builds upon LPIPS, a widely used learned perceptual similarity metric, and explores architectural design considerations to make it robust against imperceptible misalignment. Specifically, we study a wide spectrum of neural network elements, such as anti-aliasing filtering, pooling, striding, padding, and skip connection, and discuss their roles in making a robust metric. Based on our studies, we develop a new deep neural network-based perceptual similarity metric. Our experiments show that our metric is tolerant to imperceptible shifts while being consistent with the human similarity judgment.
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Code
Syntology Ran 7 of 21 code samples harvested from 1 repository linked to this paper; 14 have no recorded run. Of those that ran: 5 ran · our draft was wrong; 1 ran · fixture could not drive it; 1 ran with no contract checked.
By repository: official repository: 21 samples from 1 repository, 7 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
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Code Syntology ran Syntology
21 samples harvested; 7 ran; 0 honoured the contract we drafted; 14 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Quality Assessment | MSU SR-QA Dataset | ST-LPIPS (VGG) | KLCC | 0.45898 | #24 of 60 | Archive leaderboard | report |
| Video Quality Assessment | MSU SR-QA Dataset | ST-LPIPS (VGG) | PLCC | 0.56431 | #24 of 60 | Archive leaderboard | report |
| Video Quality Assessment | MSU SR-QA Dataset | ST-LPIPS (VGG) | SROCC | 0.57336 | #24 of 60 | Archive leaderboard | report |
| Video Quality Assessment | MSU SR-QA Dataset | ST-LPIPS (VGG) | Type | FR | #24 of 60 | Archive leaderboard | report |
| Video Quality Assessment | MSU SR-QA Dataset | ST-LPIPS (Alex) | KLCC | 0.42897 | #30 of 60 | Archive leaderboard | report |
| Video Quality Assessment | MSU SR-QA Dataset | ST-LPIPS (Alex) | PLCC | 0.54740 | #30 of 60 | Archive leaderboard | report |
| Video Quality Assessment | MSU SR-QA Dataset | ST-LPIPS (Alex) | SROCC | 0.53473 | #30 of 60 | Archive leaderboard | report |
| Video Quality Assessment | MSU SR-QA Dataset | ST-LPIPS (Alex) | Type | FR | #30 of 60 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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