A robust DIBR 3D image watermarking algorithm based on histogram shape

Abstract Depth-image-based rendering (DIBR) has become an important technology in 3D displaying. Since either of the center image and generated virtual images might be illegally distributed, we need to protect both of the two kinds of images. In this paper, a histogram shape based watermarking algorithm is proposed to protect the DIBR 3D images. To make the watermarking method robust to common attacks, a pixel mean value based pixel groups selection method is presented to select several suitable pixel groups for watermark embedding. To solve the problem that the dividing of pixel groups can affect the performance of watermark extraction, the width of pixel group is determined by the maximum difference of pixel mean value between the original and attacked images. By this way, the watermark can be extracted with lower bit error rate (BER) from the attacked watermarked image. As the experimental results shown, the proposed method is much more robust to the geometric attacks and combined attacks compared with existing methods. In addition, the proposed watermarking method also has good robustness to the adjusting of baseline distance and depth image blurred.

[1]  A. Aydin Alatan,et al.  Watermarking for depth-image-based rendering , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[2]  Christopher Joseph Pal,et al.  Learning Conditional Random Fields for Stereo , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  C.-C. Jay Kuo,et al.  An improved DC recovery method from AC coefficients of DCT-transformed images , 2010, 2010 IEEE International Conference on Image Processing.

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

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

[6]  Richard Szeliski,et al.  High-accuracy stereo depth maps using structured light , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[7]  Ja-Ling Wu,et al.  A Digital Blind Watermarking for Depth-Image-Based Rendering 3D Images , 2011, IEEE Transactions on Broadcasting.

[8]  Eun-Soo Kim,et al.  Stereo image watermarking scheme based on discrete wavelet transform and adaptive disparity estimation , 2004, SPIE Optics + Photonics.

[9]  Yong Xiang,et al.  Histogram shape-based robust image watermarking method , 2014, 2014 IEEE International Conference on Communications (ICC).

[10]  Heung-Kyu Lee,et al.  Robust DT-CWT Watermarking for DIBR 3D Images , 2012, IEEE Transactions on Broadcasting.

[11]  Toshiaki Fujii,et al.  Free viewpoint TV system based on ray-space representation , 2002, SPIE ITCom.

[12]  Heung-Yeung Shum,et al.  Image-Based Rendering and Synthesis , 2007, IEEE Signal Processing Magazine.

[13]  C. Fehn,et al.  Interactive 3-DTV-Concepts and Key Technologies , 2006 .

[14]  Christoph Fehn,et al.  Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV , 2004, IS&T/SPIE Electronic Imaging.

[15]  Heiko Hirschmüller,et al.  Evaluation of Cost Functions for Stereo Matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Heung-Kyu Lee,et al.  Perceptual Watermarking for 3D Stereoscopic Video Using Depth Information , 2011, 2011 Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[17]  Yue Zhang,et al.  Affine Legendre Moment Invariants for Image Watermarking Robust to Geometric Distortions , 2011, IEEE Transactions on Image Processing.

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

[19]  Jiwu Huang,et al.  Invariant Image Watermarking Based on Statistical Features in the Low-Frequency Domain , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..