DACNN: Blind Image Quality Assessment via a Distortion-Aware Convolutional Neural Network

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 11; pp. 7518 - 7531
• Main Authors: Pan, Zhaoqing, Zhang, Hao, Lei, Jianjun, Fang, Yuming, Shao, Xiao, Ling, Nam, Kwong, Sam
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Blind image quality assessment; Distortion; distortion-aware network; Feature extraction; Fuses; fusion network; Image quality; Knowledge engineering; Machine learning; Modules; Neural networks; Quality assessment; Semantics; Siamese network

Deep neural networks have achieved great performance on blind Image Quality Assessment (IQA), but it is still challenging for using one network to accurately predict the quality of images with different distortions. In this paper, a Distortion-Aware Convolutional Neural Network (DACNN) is proposed f...