Blind Image Quality Assessment Using Natural Scene Statistics in the Gradient Domain

An efficient, general-purpose, blind/no-reference image quality assessment (NR-IQA) algorithm based on natural image statistics in the gradient domain is proposed in this letter. We call it REFIINGS (REFerrenceless Image Integrity Notator using Gradient Statistics). The gradient of an image describes its geometric features which can be easily captured by the human visual system (HVS). In the literature, gradient-relevant methods have gotten big success in full-reference (FR) IQA and reduced-reference (RR) IQA. Inspired by these, we extend it to NR-IQA. REFIINGS utilizes the parameters of generalized Laplace distribution as part of its features, and the parameters are directly computed using given formulas which avoid parameters estimation. REFIINGS is computationally quite efficient which makes it an attractive option for the use in real-time blind assessment of visual quality. When tested on the benchmark image database, our method is quite promising.