Totally Blind Image Quality Assessment Evaluator

Most general purpose no-reference image quality assessment algorithms need prior knowledge about anticipated distortions and their corresponding human opinion scores. One or more of distortion types may not be available when creating the model. In this paper, we develop a blind/no-reference opinion unaware distortion unaware image quality assessment algorithm based on natural scenes. The proposed approach extracts features in spatial domain for both natural images and distorted image at two scales, where locally normalized luminance values are modeled in two forms: point-wise for single pixels and pair-wise based log-derivative for the relationship of adjacent pixels. Then two sharpness functions are applied whose their outputs represent the extracted features of the proposed approach. Results show that the proposed algorithm correlates well with subjective opinion scores. They also show that the proposed algorithm outperforms the full-reference PSNR and SSIM methods. Not only do the results compete well with the recently developed NIQE model, but also outperform it.