A No-Reference and Full-Reference image quality assessment and enhancement framework in real-time

These days, social media holds a large portion of our daily lives. Millions of people post their images using a social media platform. The enormous amount of images shared on social network presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to process a large amount of images in real-time while estimating and assisting in the enhancement of the No-Reference and Full-Reference image quality. Our quality evaluation is measured using a Convolutional Neural Network, which is tuned by the objective quality methods, in addition to the face alignment metric and enhanced with the help of a Super-Resolution Model. A set of experiments is conducted to evaluate our proposed approach.