Image Quality Assessment with Degradation on Spatial Structure

In this letter, we introduce an improved structural degradation based image quality assessment (IQA) method. Most of the existing structural similarity based IQA metrics mainly consider the spatial contrast degradation but have not fully considered the changes on the spatial distribution of structures. Since the human visual system (HVS) is sensitive to degradations on both spatial contrast and spatial distribution, both factors need to be considered for IQA. In order to measure the structural degradation on spatial distribution, the local binary patterns (LBPs) are first employed to extract structural information. And then, the LBP shift between the reference and distorted images is computed, because noise distorts structural patterns. Finally, the spatial contrast degradation on each pair of LBP shifts is calculated for quality assessment. Experimental results on three large benchmark databases confirm that the proposed IQA method is highly consistent with the subjective perception.

[1]  Eric C. Larson,et al.  Most apparent distortion: full-reference image quality assessment and the role of strategy , 2010, J. Electronic Imaging.

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

[3]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

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

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

[6]  Alan C. Bovik,et al.  Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures , 2009, IEEE Signal Processing Magazine.

[7]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[8]  Matti Pietikäinen,et al.  Performance evaluation of texture measures with classification based on Kullback discrimination of distributions , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[9]  Fan Zhang,et al.  Image Quality Assessment by Separately Evaluating Detail Losses and Additive Impairments , 2011, IEEE Transactions on Multimedia.

[10]  Alan C. Bovik,et al.  Image information and visual quality , 2006, IEEE Trans. Image Process..

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

[12]  Guangming Shi,et al.  Self-similarity based structural regularity for just noticeable difference estimation , 2012, J. Vis. Commun. Image Represent..

[13]  Weisi Lin,et al.  Image Quality Assessment Based on Gradient Similarity , 2012, IEEE Transactions on Image Processing.

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

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