  In most practical situations, the compression or transmission of images and
videos creates distortions that will eventually be perceived by a human
observer. Vice versa, image and video restoration techniques, such as
inpainting or denoising, aim to enhance the quality of experience of human
viewers. Correctly assessing the similarity between an image and an undistorted
reference image as subjectively experienced by a human viewer can thus lead to
significant improvements in any transmission, compression, or restoration
system. This paper introduces the Haar wavelet-based perceptual similarity
index (HaarPSI), a novel and computationally inexpensive similarity measure for
full reference image quality assessment. The HaarPSI utilizes the coefficients
obtained from a Haar wavelet decomposition to assess local similarities between
two images, as well as the relative importance of image areas. The consistency
of the HaarPSI with the human quality of experience was validated on four large
benchmark databases containing thousands of differently distorted images. On
these databases, the HaarPSI achieves higher correlations with human opinion
scores than state-of-the-art full reference similarity measures like the
structural similarity index (SSIM), the feature similarity index (FSIM), and
the visual saliency-based index (VSI). Along with the simple computational
structure and the short execution time, these experimental results suggest a
high applicability of the HaarPSI in real world tasks.
