Deep Quality: A Deep No-reference Quality Assessment System

  • Prajna Paramita Dash
  • Akshaya Mishra
  • Alexander Wong


Image quality assessment (IQA) continues to garner great interest
in the research community, particularly given the tremendous
rise in consumer video capture and streaming. Despite significant
research effort in IQA in the past few decades, the area of noreference
image quality assessment remains a great challenge and
is largely unsolved. In this paper, we propose a novel no-reference
image quality assessment system called Deep Quality, which leverages
the power of deep learning to model the complex relationship
between visual content and the perceived quality. Deep Quality
consists of a novel multi-scale deep convolutional neural network,
trained to learn to assess image quality based on training samples
consisting of different distortions and degradations such as blur,
Gaussian noise, and compression artifacts. Preliminary results using
the CSIQ benchmark image quality dataset showed that Deep
Quality was able to achieve strong quality prediction performance
(89% patch-level and 98% image-level prediction accuracy), being
able to achieve similar performance as full-reference IQA methods.

How to Cite
Dash, P., Mishra, A., & Wong, A. (2016). Deep Quality: A Deep No-reference Quality Assessment System. Journal of Computational Vision and Imaging Systems, 2(1).