Training-free referenceless camera image blur assessment via hypercomplex singular value decomposition

Abstract

Blur plays an important role in the perception of camera image quality. Generally, blur leads to attenuation of high frequency information and accordingly changes the image energy. Quaternion describes the color information as a whole. Recent researches in quaternion singular value decomposition show that the singular values and singular vectors of the quaternion can capture the distortion of color images, and thus we reasonably suppose that singular values can be utilized to evaluate the sharpness of camera images. Motivated by this, a novel training-free blind quality assessment method considering the integral color information and singular values of the distorted image is proposed to evaluate the sharpness of camera images. The blurred camera image is first converted to LAB color space and divided into blocks. Then pure quaternion is utilized to represent pixels of the blurred camera image and the energy of every block are obtained. Inspired by the human visual system appears to assess image sharpness based on the sharpest region of the image, the local sharpness normalized energy is defined as the sharpness score of the blurred camera image. Experimental results have demonstrated the effectiveness of the proposed metric compared with popular sharpness image quality metrics.