Learning data-driven image similarity measure

Abstract

Image quality assessment gains a greater interest due to development of digital imaging and storage. In that field, structural similarity (SSIM) index has been shown to favorably agree with human perceptual assessment, significantly outperforming the method of mean squared error, i.e., L 2 distance. The similarity measure function in SSIM which compares a target (distorted) image with its reference (original) image is handcrafted in a simple form via a top-down approach based on the human visual system. It, however, might lack optimality without directly considering the relationships between image data and the perceptual assessment (scores). In this paper, we propose a method to construct an image similarity measure based on actual data. The proposed method optimizes a similarity measure function by exploiting annotated data in a bottom-up and data-driven manner, while retaining the favorable property of structural similarity in SSIM. The non-linear similarity function is optimized as the global optimum of high generalization power. In addition, the proposed method is simply formulated and thus applicable to the family of SSIM, especially to FSIM which has been recently proposed exhibiting superior performance to SSIM. The experimental results on image quality assessment demonstrate the effectiveness of the proposed method compared to the other methods.