HaarPSI

The Haar wavelet-based perceptual similarity index (HaarPSI) is a similarity measure for images that aims to correctly assess the perceptual similarity between two images with respect to a human viewer.

In most practical situations, images and videos can neither be compressed nor transmitted without introducing distortions that will eventually be perceived by a human observer. Vice versa, most applications of image and video restoration techniques, such as inpainting or denoising, aim to enhance the quality of experience of human viewers. Correctly predicting the similarity of an image with an undistorted reference image, as subjectively experienced by a human viewer, can thus lead to significant improvements in any transmission, compression, or restoration system.

Acknowledgments

The HaarPSI was first proposed in

R. Reisenhofer, S. Bosse, G. Kutyniok and T. Wiegand.
A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment. Signal Processing: Image Communication, vol. 61, 33-43, 2018.
<a href="https://doi.org/10.1016/j.image.2017.11.001">doi:10.1016/j.image.2017.11.001</a>

Please cite this paper if you use the HaarPSI in your research.

Authors

Rafael Reisenhofer - HarPSI.m and HaarPSIExt.m
David Neumann (lecode-official) - haarPsi.py

License

This project is licensed under the MIT License - see the LICENSE file for details.