Boundary Artifact Reduction in View Synthesis of 3D Video: From Perspective of Texture-Depth Alignment

3D Video (3DV) with depth-image-based view synthesis is a promising candidate of next generation broadcasting applications. However, the synthesized views in 3DV are often contaminated by annoying artifacts, particularly notably around object boundaries, due to imperfect depth maps (e.g., produced by state-of-the-art stereo matching algorithms or compressed lossily). In this paper, we first review some representative methods for boundary artifact reduction in view synthesis, and make an in-depth investigation into the underlying mechanisms of boundary artifact generation from a new perspective of texture-depth alignment in boundary regions. Three forms of texture-depth misalignment are identified as the causes for different boundary artifacts, which mainly present themselves as scattered noises on the background and object erosion on the foreground. Based on the insights gained from the analysis, we propose a novel solution of suppression of misalignment and alignment enforcement (denoted as SMART) between texture and depth to reduce background noises and foreground erosion, respectively, among different types of boundary artifacts. The SMART is developed as a three-step pre-processing in view synthesis. Experiments on view synthesis with original and compressed texture/depth data consistently demonstrate the superior performance of the proposed method as compared with other relevant boundary artifact reduction schemes.

[1]  Ismo Rakkolainen,et al.  A Survey of 3DTV Displays: Techniques and Technologies , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Wijnand A. IJsselsteijn,et al.  Perceived quality of compressed stereoscopic images: Effects of symmetric and asymmetric JPEG coding and camera separation , 2006, TAP.

[4]  Toshiaki Fujii,et al.  View Generation with 3D Warping Using Depth Information for FTV , 2008, 2008 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video.

[5]  Liang Zhang,et al.  Stereoscopic image generation based on depth images for 3D TV , 2005, IEEE Transactions on Broadcasting.

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

[7]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Masayuki Tanimoto,et al.  Multiview Imaging and 3DTV , 2007, IEEE Signal Processing Magazine.

[9]  Aljoscha Smolic,et al.  View Synthesis for Advanced 3D Video Systems , 2008, EURASIP J. Image Video Process..

[10]  Marek Domanski,et al.  View Synthesis for Multiview Video Transmission , 2009, IPCV.

[11]  Toshiaki Fujii,et al.  Error supression in view synthesis using reliability reasoning for FTV , 2010, 2010 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video.

[12]  Toshiaki Fujii,et al.  Artifact reduction using reliability reasoning for image generation of FTV , 2010, J. Vis. Commun. Image Represent..

[13]  Stefan Winkler,et al.  The Evolution of Video Quality Measurement: From PSNR to Hybrid Metrics , 2008, IEEE Transactions on Broadcasting.

[14]  Yo-Sung Ho,et al.  Hole filling method using depth based in-painting for view synthesis in free viewpoint television and 3-D video , 2009, 2009 Picture Coding Symposium.

[15]  Lu Yu,et al.  A perceptual metric for evaluating quality of synthesized sequences in 3DV system , 2010, Visual Communications and Image Processing.

[16]  T. Wiegand,et al.  The Effect of Depth Compression on Multiview Rendering Quality , 2008, 2008 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video.

[17]  Gauthier Lafruit,et al.  Interpolation error as a quality metric for stereo: Robust, or not? , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[18]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[19]  Lu Yu,et al.  Temporal consistency enhancement on depth sequences , 2010, 28th Picture Coding Symposium.

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

[21]  David Salesin,et al.  A Bayesian approach to digital matting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[22]  Dong Tian,et al.  View synthesis techniques for 3D video , 2009, Optical Engineering + Applications.

[23]  Yo-Sung Ho,et al.  Boundary Filtering on Synthesized Views of 3D Video , 2008, 2008 Second International Conference on Future Generation Communication and Networking Symposia.

[24]  C. Fehn A 3 DTV Approach Using Depth-Image-Based Rendering ( DIBR ) , 2003 .

[25]  Aljoscha Smolic,et al.  The effects of multiview depth video compression on multiview rendering , 2009, Signal Process. Image Commun..