Deep Quality: A Deep No-reference Quality Assessment System

Abstract

Image quality assessment (IQA) continues to garner great interestin the research community, particularly given the tremendousrise in consumer video capture and streaming. Despite significantresearch effort in IQA in the past few decades, the area of noreferenceimage quality assessment remains a great challenge andis largely unsolved. In this paper, we propose a novel no-referenceimage quality assessment system called Deep Quality, which leveragesthe power of deep learning to model the complex relationshipbetween visual content and the perceived quality. Deep Qualityconsists of a novel multi-scale deep convolutional neural network,trained to learn to assess image quality based on training samplesconsisting of different distortions and degradations such as blur,Gaussian noise, and compression artifacts. Preliminary results usingthe CSIQ benchmark image quality dataset showed that DeepQuality was able to achieve strong quality prediction performance(89% patch-level and 98% image-level prediction accuracy), beingable to achieve similar performance as full-reference IQA methods.