No-reference image quality assessment in contourlet domain

  • Authors:

  • Wen Lu;Kai Zeng;Dacheng Tao;Yuan Yuan;Xinbo Gao

  • Affiliations:

  • School of Electronic Engineering, Xidian University, Xi'an 710071, China;School of Electronic Engineering, Xidian University, Xi'an 710071, China;School of Computer Engineering, Nanyang Technological University, 50 Nanyang, Avenue, 639798 Singapore, Singapore;School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, UK;School of Electronic Engineering, Xidian University, Xi'an 710071, China

  • Venue:
  • Neurocomputing
  • Year:
  • 2010

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Abstract

The target of no-reference (NR) image quality assessment (IQA) is to establish a computational model to predict the visual quality of an image. The existing prominent method is based on natural scene statistics (NSS). It uses the joint and marginal distributions of wavelet coefficients for IQA. However, this method is only applicable to JPEG2000 compressed images. Since the wavelet transform fails to capture the directional information of images, an improved NSS model is established by contourlets. In this paper, the contourlet transform is utilized to NSS of images, and then the relationship of contourlet coefficients is represented by the joint distribution. The statistics of contourlet coefficients are applicable to indicate variation of image quality. In addition, an image-dependent threshold is adopted to reduce the effect of content to the statistical model. Finally, image quality can be evaluated by combining the extracted features in each subband nonlinearly. Our algorithm is trained and tested on the LIVE database II. Experimental results demonstrate that the proposed algorithm is superior to the conventional NSS model and can be applied to different distortions.