A Haar wavelet-based perceptual similarity index for image quality assessment

By Rafael Reisenhofer, Sebastian Bosse, Gitta Kutyniok, Thomas Wiegand. Signal processing. Image communication, 2016

322 citations recorded by the source corpus.

Abstract 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.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/e3c0c5be84738292bc6cdb7d2df054076331ee67

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment (2016, methodology): “The Haar wavelet-based perceptual similarity index (HaarPSI) [11] employs a similar kind of pooling, as local similarity is computed based on the Haar wavelet representation of reference and distorted image and locally weighted based on a visual activity measure that is calculated from the same…”
PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference (2018, methodology): “…visual system (HVS) such as contrast sensitivity [22], high-level structural acuity [52], and masking [48, 51], or use other statistics/features [3,4,14,15,38,44–46, 53, To address these limitations, some have proposed IQA methods based on machine learning to learn more sophisticated models [19].”