A ghosting artifact detector for interpolated image quality assessment

We present a no-reference image quality metric for image interpolation. The approach is capable of detecting blurry regions as well as ghosting artifacts, e.g., in image based rendering scenarios. Based on the assumption that ghosting artifacts can be detected locally, perceived visual quality can be predicted from the amount of regions that are affected by ghosting. Because the approach does not require any reference image, it is very suitable, e.g., for assessing quality of image-based rendering techniques in general settings.

[1]  Adrian Hilton,et al.  Objective Quality Assessment in Free-Viewpoint Video Production , 2008, 3DTV-CON 2008.

[2]  Huamin Wang,et al.  Space-Time Light Field Rendering , 2007, IEEE Transactions on Visualization and Computer Graphics.

[3]  Adrian Hilton,et al.  Surface Capture for Performance-Based Animation , 2007, IEEE Computer Graphics and Applications.

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

[5]  Ingrid Heynderickx,et al.  A no-reference perceptual blockiness metric , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

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

[8]  Wojciech Matusik,et al.  3D TV: a scalable system for real-time acquisition, transmission, and autostereoscopic display of dynamic scenes , 2004, ACM Trans. Graph..

[9]  H.-J. Zepernick,et al.  Perceptual-based Quality Metrics for Image and Video Services: A Survey , 2007, 2007 Next Generation Internet Networks.

[10]  Zhou Wang,et al.  Quality-aware images , 2006, IEEE Transactions on Image Processing.

[11]  Sanjit K. Mitra,et al.  No-reference video quality metric based on artifact measurements , 2005, IEEE International Conference on Image Processing 2005.

[12]  Richard Szeliski,et al.  High-quality video view interpolation using a layered representation , 2004, SIGGRAPH 2004.

[13]  Takeo Kanade,et al.  Image-based spatio-temporal modeling and view interpolation of dynamic events , 2005, TOGS.

[14]  Bobby Bodenheimer,et al.  Synthesis and evaluation of linear motion transitions , 2008, TOGS.

[15]  Hans-Peter Seidel,et al.  Performance capture from sparse multi-view video , 2008, SIGGRAPH 2008.

[16]  Marc Levoy,et al.  Light field rendering , 1996, SIGGRAPH.

[17]  Jean-Yves Guillemaut,et al.  Objective Quality Assessment in Free-Viewpoint Video Production , 2008, 2008 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video.

[18]  Marcus A. Magnor,et al.  View and Time Interpolation in Image Space , 2008, Comput. Graph. Forum.

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

[20]  Marcus A. Magnor,et al.  Perception-motivated interpolation of image sequences , 2008, APGV.