LPIPS metric. pip install lpips

A metric for Perceptual Image-Error Assessment through Pairwise Preference (PieAPP at CVPR 2018).

A simple and useful implementation of LPIPS.

Comparing different similarity functions for reconstruction of image on CycleGAN. (https://tandon-a.github.io/CycleGAN_ssim/) Training cycleGAN with different loss functions to improve visual quality of produced images

[ECCV 2022] We investigated a broad range of neural network elements and developed a robust perceptual similarity metric. Our shift-tolerant perceptual similarity metric (ST-LPIPS) is consistent with human perception and is less susceptible to imperceptible misalignments between two images than existing metrics.

Experiments with perceptual loss and autoencoders.

A no-reference version of HDR-VDP using deep-learning

LPIPS metric on PaddlePaddle. pip install paddle-lpips

A Study of Deep Perceptual Metrics for Image quality Assessment

Benchmarking library for image manipulation detection.

[TMLR 2023] as a featured article (spotlight 🌟 or top 0.01% of the accepted papers). In this study, we systematically examine the robustness of both traditional and learned perceptual similarity metrics to imperceptible adversarial perturbations.

Finetuning and clustering library for image perceptual similarity models.

Android librarry (kotlin) : Image (JPEG, BMP) comparison (perceptual hash algorithm)

Clustering slices within NIfTI volume based on Perceptual Similarity or SSIM.