Spatial domain synthetic scene statistics
Natural Scene Statistics (NSS) has been applied to natural images obtained through optical cameras for automated visual quality assessment. Since NSS does not need a reference image for comparison, NSS has been used to assess user quality-of-experience, such as for streaming wireless image and video content acquired by cameras. In this paper, we take an important first step in using NSS to automate visual quality assessment of synthetic images found in video games and animated movies. In particular, we analyze NSS for synthetic images in the spatial domain using mean-subtracted-contrast-normalized (MSCN) pixels and their gradients. The primary contributions of this paper are (1) creation of a publicly available ESPL Synthetic Image database, containing 221 color images, mostly in high definition resolution of 1920 × 1080, and (2) analysis of the statistical distributions of the MSCN coefficients (and their gradients) for synthetic images, obtained from the image intensities. We find that similar to the case for natural images, the distributions of the MSCN pixels for synthetic images can be modeled closely by Generalized Gaussian and Symmetric α-Stable distributions, with slightly different shape and scale parameters.