Reduced-reference image quality assessment with local binary structural pattern

Reduced-reference (RR) image quality assessment (IQA) aims to use less reference data and achieve higher quality prediction accuracy. Recent researches confirm that the human visual system (HVS) is adapted to extract structural information and is sensitive to structure degradation. Therefore, in this paper, we try to represent image contents with several structural patterns, and measure image quality according to the structural degradation on these patterns. The classic local binary patterns (LBPs) are firstly employed to extract image structures and create LBP based structural histogram. And then, the structural degradation is computed as the histogram distance between the reference and distorted images. Experimental results on three large databases demonstrate that the proposed RR IQA method greatly improved the quality prediction accuracy.

[1]  Guangming Shi,et al.  Reduced-Reference Image Quality Assessment With Visual Information Fidelity , 2013, IEEE Transactions on Multimedia.

[2]  D. Knill,et al.  The Bayesian brain: the role of uncertainty in neural coding and computation , 2004, Trends in Neurosciences.

[3]  Karl J. Friston The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[4]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Zhou Wang,et al.  Reduced-reference image quality assessment using a wavelet-domain natural image statistic model , 2005, IS&T/SPIE Electronic Imaging.

[6]  Alan C. Bovik,et al.  RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment , 2012, IEEE Transactions on Image Processing.

[7]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[8]  Alan C. Bovik,et al.  Image and Video Quality Assessment , 2008, Encyclopedia of Multimedia.

[9]  Guangming Shi,et al.  Perceptual Quality Metric With Internal Generative Mechanism , 2013, IEEE Transactions on Image Processing.

[10]  Alan C. Bovik,et al.  No-reference quality assessment using natural scene statistics: JPEG2000 , 2005, IEEE Transactions on Image Processing.

[11]  Weisi Lin,et al.  Perceptual visual quality metrics: A survey , 2011, J. Vis. Commun. Image Represent..

[12]  Xuelong Li,et al.  Image Quality Assessment Based on Multiscale Geometric Analysis , 2009, IEEE Transactions on Image Processing.

[13]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.