CN-BSRIQA: Cascaded network- blind super-resolution image quality assessment

High resolution (HR) images consist of higher quality and more detail information in comparison to lowresolution images. But obtaining HR images entails higher costs and requires a larger workforce. The solution is using super -resolution (SR) images. Single image SR is the process of creating HR images from a single lowresolution image. Obtaining SR images from low -resolution images is a well-known problem in the domains of computer vision and image processing. Advancement in technology has given rise to many SR algorithms. The perceptual quality assessment of super -resolved images can be used to benchmark the techniques employed for image SR. In this work, a cascaded network -blind super -resolution image quality assessment (CN-BSRIQA) methodology is proposed. The proposed approach works under the cascaded architecture where a convolutional neural network (CNN) is cascaded with a deep belief network (DBN). CNN is employed as a shallow network in the proposed methodology to extract low-level information after partitioning the input image into patches. To assess the visual quality of the SR images, the features retrieved from CNN are incorporated into a DBN. Three databases are used to evaluate the performance of proposed CN-BSRIQA i.e., SR Quality Database (SRQD), SR Image Quality Database (SRID), and visual quality evaluation for super -resolved images (QADS). When compared to other state-of-the-art methodologies for assessing the visual quality of SR images, CN-BSRIQA outperforms them. For the perceptual quality assessment of SR images, the experimental results reveal that CNN -based techniques outperform techniques based on hand-crafted features. Furthermore, the shallow CNN proposed in the CN-BSRIQA can extract features that are content -independent i.e., they show better performance over cross -database evaluation in comparison to existing state-of-the-art techniques.