A full-reference stereoscopic image quality metric based on binocular energy and regression analysis

The recent developments of 3D media technology have brought to life numerous applications of interactive entertainment such as 3D cinema, 3DTV and gaming. However, due to the data intensive nature of 3D visual content, a number of research challenges have emerged. In order to optimise the end-to-end content life-cycle, from capture to processing and delivery, Quality of Experience (QoE) has become a major driving factor. This paper presents a human-centric approach to quality estimation of 3D visual content. A fullreference quality assessment method for stereoscopic images is proposed. It is based on a Human Visual System (HVS) model to estimate subjective scores of registered stereoscopic images subjected to compression losses. The model has been trained with four publicly available registered stereoscopic image databases and a fixed relationship between subjective scores and the model has been determined. The high correlation of the relationship over a large number of stimuli has proven its consistency over the state-of-the-art.

[1]  David J. Fleet,et al.  Neural encoding of binocular disparity: Energy models, position shifts and phase shifts , 1996, Vision Research.

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

[3]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[4]  Patrick Le Callet,et al.  Quality Assessment of Stereoscopic Images , 2008, EURASIP J. Image Video Process..

[5]  Touradj Ebrahimi,et al.  Perceptually driven 3D distance metrics with application to watermarking , 2006, SPIE Optics + Photonics.

[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]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[8]  G. Peyré.  Géométrie multi-échelles pour les images et les textures , 2005 .

[9]  Wijnand A. IJsselsteijn,et al.  A survey of perceptual evaluations and requirements of three-dimensional TV , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  S. Mallat,et al.  Orthogonal bandelet bases for geometric images approximation , 2008 .

[11]  R. Shapley,et al.  An egalitarian network model for the emergence of simple and complex cells in visual cortex , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[13]  Ahmet M. Kondoz,et al.  Toward an Impairment Metric for Stereoscopic Video: A Full-Reference Video Quality Metric to Assess Compressed Stereoscopic Video , 2013, IEEE Transactions on Image Processing.

[14]  Ahmet M. Kondoz,et al.  An improved model of binocular energy calculation for full-reference stereoscopic image quality assessment , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Junyong You,et al.  PERCEPTUAL QUALITY ASSESSMENT FOR STEREOSCOPIC IMAGES BASED ON 2 D IMAGE QUALITY METRICS AND DISPARITY ANALYSIS , 2010 .

[16]  Roushain Akhter,et al.  No-reference stereoscopic image quality assessment , 2010, Electronic Imaging.

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

[18]  Gregory C. DeAngelis,et al.  Depth is encoded in the visual cortex by a specialized receptive field structure , 1991, Nature.