Using Binocular Feature Combination for Blind Quality Assessment of Stereoscopic Images

The quality assessment of 3D images is more challenging than its 2D counterparts, and little investigation has been dedicated to blind quality assessment of stereoscopic images. In this letter, we propose a novel blind quality assessment for stereoscopic images based on binocular feature combination. The prominent contribution of this work is that we simplify the process of binocular quality prediction as monocular feature encoding and binocular feature combination. Experimental results on two publicly available 3D image quality assessment databases demonstrate the promising performance of the proposed method.

[1]  Lei Zhang,et al.  Learning without Human Scores for Blind Image Quality Assessment , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Hongyu Li,et al.  Training Quality-Aware Filters for No-Reference Image Quality Assessment , 2014, IEEE MultiMedia.

[3]  Do-Kyoung Kwon,et al.  Full-reference quality assessment of stereopairs accounting for rivalry , 2013, Signal Process. Image Commun..

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

[5]  David S. Doermann,et al.  No-Reference Image Quality Assessment Using Visual Codebooks , 2012, IEEE Transactions on Image Processing.

[6]  Mohamed-Chaker Larabi,et al.  A perceptual metric for stereoscopic image quality assessment based on the binocular energy , 2013, Multidimens. Syst. Signal Process..

[7]  Alan C. Bovik,et al.  Subjective evaluation of stereoscopic image quality , 2013, Signal Process. Image Commun..

[8]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[9]  Rama Chellappa,et al.  Joint Sparse Representation for Robust Multimodal Biometrics Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Alan C. Bovik,et al.  Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality , 2011, IEEE Transactions on Image Processing.

[11]  Weisi Lin,et al.  Perceptual Full-Reference Quality Assessment of Stereoscopic Images by Considering Binocular Visual Characteristics , 2013, IEEE Transactions on Image Processing.

[12]  Mohamed-Chaker Larabi,et al.  Binocular Energy Estimation Based on Properties of the Human Visual System , 2012, Cognitive Computation.

[13]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[14]  Mei Yu,et al.  Subjective quality analyses of stereoscopic images in 3DTV system , 2011, 2011 Visual Communications and Image Processing (VCIP).

[15]  Wenjun Zhang,et al.  No-Reference Stereoscopic IQA Approach: From Nonlinear Effect to Parallax Compensation , 2012, J. Electr. Comput. Eng..

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

[17]  Ja-Ling Wu,et al.  Quality Assessment of Stereoscopic 3D Image Compression by Binocular Integration Behaviors , 2014, IEEE Transactions on Image Processing.

[18]  Kwanghoon Sohn,et al.  No-Reference Quality Assessment for Stereoscopic Images Based on Binocular Quality Perception , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  Alan C. Bovik,et al.  No-Reference Quality Assessment of Natural Stereopairs , 2013, IEEE Transactions on Image Processing.

[20]  Christophe Charrier,et al.  Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain , 2012, IEEE Transactions on Image Processing.

[21]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[22]  George Sperling,et al.  A gain-control theory of binocular combination. , 2006, Proceedings of the National Academy of Sciences of the United States of America.