Efficient automatic detection of 3D video artifacts

This paper summarizes some common artifacts in stereo video content. These artifacts lead to poor even uncomfortable 3D viewing experience. Efficient approaches for detecting three typical artifacts, sharpness mismatch, synchronization mismatch and stereoscopic window violation, are presented in detail. Sharpness mismatch is estimated by measuring the width deviations of edge pairs in depth planes. Synchronization mismatch is detected based on the motion inconsistencies of feature points between the stereoscopic channels in a short time frame. Stereoscopic window violation is detected, using connected component analysis, when objects hit the vertical frame boundaries while being in front of the virtual screen. For experiments, test sequences were created in a professional studio environment and state-of-the-art metrics were used for evaluating the proposed approaches. The experimental results show that our algorithms have considerable robustness in detecting 3D defects.

[1]  Oliver Schreer,et al.  Stereo analysis by hybrid recursive matching for real-time immersive video conferencing , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Bernard Mendiburu,et al.  3D TV and 3D Cinema: Tools and Processes for Creative Stereoscopy , 2011 .

[3]  Moustafa Yaqub,et al.  Visual fields interpretation in glaucoma: a focus on static automated perimetry , 2013, Community eye health.

[4]  Lina J. Karam,et al.  A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD) , 2011, IEEE Transactions on Image Processing.

[5]  Michael Stuart,et al.  Understanding Robust and Exploratory Data Analysis , 1984 .

[6]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[7]  ITU-T Rec. P.910 (04/2008) Subjective video quality assessment methods for multimedia applications , 2009 .

[8]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[9]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[10]  T Sakamoto,et al.  Model for spherical aberration in a single radial gradient-rod lens. , 1984, Applied optics.

[11]  Stefan Winkler,et al.  Perceptual blur and ringing metrics: application to JPEG2000 , 2004, Signal Process. Image Commun..

[12]  Mikko Nuutinen,et al.  Spatial and Temporal Information as Camera Parameters for Super-resolution Video , 2012, 2012 IEEE International Symposium on Multimedia.

[13]  Lina J. Karam,et al.  A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB) , 2009, IEEE Transactions on Image Processing.

[14]  Bernard Mendiburu,et al.  3D Movie Making: Stereoscopic Digital Cinema from Script to Screen , 2009 .

[15]  Touradj Ebrahimi,et al.  Subjective and Objective Visual Quality Assessment in the Context of Stereoscopic 3D-TV , 2013 .