A grouplet-based reduced reference image quality assessment
Aldo Maalouf, Mohamed–Chaker Larabi, Christine Fernández-Maloigne · 2009
The past decades have witnessed the tremendous growth of digital image processing techniques for visual information representation and communication. Particularly, computational representation of perceived image quality has become a fundamental problem in computer vision and image processing. It is well known that the commonly used peak signal-to-noise ratio (PSNR), although analysis friendly, falls far short of this need. In this work, we propose a reduced reference (RR) perceptual image quality measure (IQM) based on the grouplet transform. Given a reference image and its "distorted" version, we first compute the grouplet transform in order to extract the information of textures and directions of both images. Then, contrast sensitivity function (CSF) filtering is performed to obtain same visual sensitivity information within both images. Thereafter, based on the properties of the human visual system (HVS), rational sensitivity thresholding is performed to obtain the sensitivity coefficients of both images. Finally, RR image quality assessment (IQA) is performed by comparing the sensitivity coefficients of both images.