Understanding How Image Quality Affects Deep Neural Networks
This addresses the practical problem of image quality variability in machine vision systems for developers and researchers, but it is incremental as it focuses on evaluation rather than proposing new solutions.
The paper evaluated four state-of-the-art deep neural network models for image classification under five types of quality distortions (blur, noise, contrast, JPEG, and JPEG2000 compression), finding that these networks are susceptible to distortions, especially blur and noise.
Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high quality image datasets, yet in practical applications the input images can not be assumed to be of high quality. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In this paper we provide an evaluation of 4 state-of-the-art deep neural network models for image classification under quality distortions. We consider five types of quality distortions: blur, noise, contrast, JPEG, and JPEG2000 compression. We show that the existing networks are susceptible to these quality distortions, particularly to blur and noise. These results enable future work in developing deep neural networks that are more invariant to quality distortions.