Full-reference IPTV image quality assessment by deeply learning structural cues

Image quality assessment (IQA) attempts to quantify the quality-aware visual attributes perceived by humans. They can be divided into subjective and objective image quality assessment. Subjective IQA algorithms rely on human judgment of image quality, where the human visual perception functions as the dominant factor However, they cannot be widely applied in practice due to the heavy reliance on different individuals. Motivated by the fact that objective IQA largely depends on image structural information, we propose a structural cues-based full-reference IPTV IQA algorithm. More specifically, we first design a grid-based object detection module to extract multiple structural information from both the reference IPTV image (i.e., video frame) and the test one. Afterwards, we propose a structure-preserved deep neural networks to generate the deep representation for each IPTV image. Subsequently, a new distance metric is proposed to measure the similarity between the reference image and the evaluated image. A test IPV image with a small calculated distance is considered as a high quality one. Comprehensive comparative study with the state-of-the-art IQA algorithms have shown that our method is accurate and robust.