A Novel Patch Variance Biased Convolutional Neural Network for No-Reference Image Quality Assessment

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

24 Scopus Citations
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Author(s)

  • Wilson Y. F. Yuen
  • Yuming Li
  • Xuyuan Xu
  • Peter H. W. Wong
  • Hon-Tung Luk

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1223-1229
Journal / PublicationIEEE Transactions on Circuits and Systems for Video Technology
Volume29
Issue number4
Online published7 Jan 2019
Publication statusPublished - Apr 2019

Abstract

Deep Convolutional Neural Networks (CNNs) have been successfully applied on no-reference image quality assessment (NR-IQA) with respect to human perception. Most of these methods deal with small image patches and use the average score of the test patches for predicting the whole image quality. We discovered that image patches from homogenous regions are unreliable for both neural network training and final image quality score estimation. In addition, image patches with complex structures have much higher chances to achieve better image quality prediction. Based on these findings, we enhanced the conventional CNN-based NR-IQA algorithm to avoid homogenous patches for the network training and quality score estimation. Moreover, we also use a variance-based weighting average to bias the final image quality score to the patches with complex structure. Experimental results show that this simple approach can achieve state-of-the-art performance as compared with well-known NR-IQA algorithms.

Research Area(s)

  • Convolution, convolution neural network, Convolutional neural networks, deep learning, Estimation, Image color analysis, Image quality, no-reference image quality assessment, Training

Citation Format(s)

A Novel Patch Variance Biased Convolutional Neural Network for No-Reference Image Quality Assessment. / Po, Lai-Man; Liu, Mengyang; Yuen, Wilson Y. F. et al.

In: IEEE Transactions on Circuits and Systems for Video Technology, Vol. 29, No. 4, 04.2019, p. 1223-1229.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review