title: 3D-MAD: A Full Reference Stereoscopic Image Quality Estimator Based on Binocular Lightness and Contrast Perception.
abstract: Algorithms for a stereoscopic image quality assessment (IQA) aim to estimate the qualities of 3D images in a manner that agrees with human judgments. The modern stereoscopic IQA algorithms often apply 2D IQA algorithms on stereoscopic views, disparity maps, and/or cyclopean images, to yield an overall quality estimate based on the properties of the human visual system. This paper presents an extension of our previous 2D most apparent distortion (MAD) algorithm to a 3D version (3D-MAD) to evaluate 3D image quality. The 3D-MAD operates via two main stages, which estimate perceived quality degradation due to 1) distortion of the monocular views and 2) distortion of the cyclopean view. In the first stage, the conventional MAD algorithm is applied on the two monocular views, and then the combined binocular quality is estimated via a weighted sum of the two estimates, where the weights are determined based on a block-based contrast measure. In the second stage, intermediate maps corresponding to the lightness distance and the pixel-based contrast are generated based on a multipathway contrast gain-control model. Then, the cyclopean view quality is estimated by measuring the statistical-difference-based features obtained from the reference stereopair and the distorted stereopair, respectively. Finally, the estimates obtained from the two stages are combined to yield an overall quality score of the stereoscopic image. Tests on various 3D image quality databases demonstrate that our algorithm significantly improves upon many other state-of-the-art 2D/3D IQA algorithms.
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhang
authors.forename: Damon M
authors.initials: DM
authors.lastname: Chandler
mesh_terms: D000465:Algorithms; D015350:Contrast Sensitivity; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D000069550:Machine Learning; D008954:Models, Biological; D010780:Photogrammetry / Q000379:methods*; D060388:Support Vector Machine