Complex Singular Value Decomposition based Stereoscopic Image Quality Assessment

Designing a reliable and generic perceptual quality metric is a challenging issue in three-dimensional (3D) visual signal processing. Due to the limited knowledge on 3D perceptual, it is difficult to fuse the visual information of left and right views in an effective way. In this paper, we propose a complex singular value decomposition (CSVD) based stereoscopic image quality assessment (SIQA) metric. First, the corresponding blocks of the left/right view are grouped into complex representation (CR) block through the scale-invariant feature transform (SIFT) view matching process. Then we compute the CSVD coefficients of each CR block. Final, a CSVD based quality pooling stage is employed to predict the final visual quality of the distorted 3D image. Experimental results demonstrate that the proposed metric has good consistency with 3D perception of human.