Perceptual Information Fidelity for Quality Estimation of Industrial Images

Abstract

Depending on high quality images, industrial vision technologies can basically oversee all the industrial production processes, such as workpiece processing and assembly automation, which play a highly significant role in promoting detection automation and production capacity in assembly lines. Unlike the natural scene images which consist of richer colors and natural lines, industrial images that cover complex industrial goods and equipment are made up of fewer colors, more regular shapes, massive graphic elements, etc., causing existing image processing methods for quality estimation, enhancement and monitoring to fail. Human beings usually play the part of the final receiver of an industrial image, so in the researches of image quality estimation, it is necessary to take the perception process of human eyes and brain to the input images into consideration. On this basis, we in this paper propose a novel perceptual information fidelity based image quality estimation model, abbreviated as PIF. Particularly, we first introduce a visual-cell low-pass filter and an optical-nerve noise model, which are separately inspired by the two processes: one is that an image in the form of optical signals arrives at the retina through the eye’s optical system to form the stimuli; the other is that the aforesaid stimuli in the form of electrical signals transfer to the human brain through the optical nerve. Second, we construct a novel image content-aware adjustor to optimize the above visual-cell low-pass filter and optical-nerve noise model. Third, we compare the two quantities of the information that is present in the clean image and how much of the information can be extracted from the lossy image to generate the overall quality score. Experiments on the two large-size industrial image quality databases demonstrate the excellent performance achieved by our proposed PIF model, with a remarkable performance gain over the existing state-of-the-art competitors.