  Blind or no-reference image quality assessment (NR-IQA) is a fundamental,
unsolved, and yet challenging problem due to the unavailability of a reference
image. It is vital to the streaming and social media industries that impact
billions of viewers daily. Although previous NR-IQA methods leveraged different
feature extraction approaches, the performance bottleneck still exists. In this
paper, we propose a simple and yet effective general-purpose no-reference (NR)
image quality assessment (IQA) framework based on multi-task learning. Our
model employs distortion types as well as subjective human scores to predict
image quality. We propose a feature fusion method to utilize distortion
information to improve the quality score estimation task. In our experiments,
we demonstrate that by utilizing multi-task learning and our proposed feature
fusion method, our model yields better performance for the NR-IQA task. To
demonstrate the effectiveness of our approach, we test our approach on seven
standard datasets and show that we achieve state-of-the-art results on various
datasets.
