Nonsubsampled contourlet transform-based algorithm for no-reference image quality assessment

Abstract

No-reference image quality assessment (NRIQA) is to be used when a reference image may not be available. The existing well-known NRIQA algorithms are those that use natural scene statistics (NSS), such as NSS model in wavelet domain (WNSS) and NSS model in contourlet domain (CNSS). WNSS is only applicable to evaluate JPEG2000 compressed images, while CNSS can be used to evaluate five different distortion types of image sets such as JPEG, JPEG2000, white noise, Gaussian blur, and fast fading. However, due to the downsamplers and upsamplers, the contourlet transform is not shift-invariant and may cause pseudo-Gibbs phenomena. In this paper, we propose an improved NRIQA based on the nonsubsampled contourlet transform (NSCT) which has shift-invariant characteristics, multiscale, and multidirection expansion. The basic observation of the proposed algorithm is that in the nonsubsampled contourlet domain natural images exhibit certain common joint statistical characteristics which can be represented by a mathematical model and disturbed by a wide variety of distortions. Performance evaluation tests show that the predicted quality scores obtained by the proposed algorithm are more effective and consistent with visual quality than those by WNSS or CNSS-based NRIQA on four distortion types of image sets in the LIVE image database except for JPEG2000 compressed images.