TY - GEN
T1 - Understanding how image quality affects deep neural networks
AU - Dodge, Samuel
AU - Karam, Lina
PY - 2016/6/23
Y1 - 2016/6/23
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84979701671&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84979701671&partnerID=8YFLogxK
U2 - 10.1109/QoMEX.2016.7498955
DO - 10.1109/QoMEX.2016.7498955
M3 - Conference contribution
AN - SCOPUS:84979701671
T3 - 2016 8th International Conference on Quality of Multimedia Experience, QoMEX 2016
BT - 2016 8th International Conference on Quality of Multimedia Experience, QoMEX 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Quality of Multimedia Experience, QoMEX 2016
Y2 - 6 June 2016 through 8 June 2016
ER -