基于左右视点相关性的立体图像鲁棒水印方法

张芳燕, 骆挺, 蒋刚毅, 等. 基于左右视点相关性的立体图像鲁棒水印方法[J]. 光电工程, 2018, 45(12): 180054. doi: 10.12086/oee.2018.180054
引用本文: 张芳燕, 骆挺, 蒋刚毅, 等. 基于左右视点相关性的立体图像鲁棒水印方法[J]. 光电工程, 2018, 45(12): 180054. doi: 10.12086/oee.2018.180054
Zhang Fangyan, Luo Ting, Jiang Gangyi, et al. Robust stereo images watermarking based on correlations of left and right views[J]. Opto-Electronic Engineering, 2018, 45(12): 180054. doi: 10.12086/oee.2018.180054
Citation: Zhang Fangyan, Luo Ting, Jiang Gangyi, et al. Robust stereo images watermarking based on correlations of left and right views[J]. Opto-Electronic Engineering, 2018, 45(12): 180054. doi: 10.12086/oee.2018.180054

基于左右视点相关性的立体图像鲁棒水印方法

  • 基金项目:
    国家自然科学基金项目(61501270);宁波市自然科学基金项目(2017A610127)
详细信息
    作者简介:
    通讯作者: 骆挺(1980-),男,博士,副教授,主要从事多媒体通信、信息隐藏的研究。E-mail: luoting@nbu.edu.cn
  • 中图分类号: TP391

Robust stereo images watermarking based on correlations of left and right views

  • Fund Project: Supported by National Natural Science Foundation of China (61501270) and Natural Science Foundation of Ningbo(2017A610127)
More Information
  • 针对立体图像版权保护问题,提出了一种基于左右视点相关性的立体图像鲁棒水印方法。由于张量分解能够较好保存图像的主要能量,因此,首先利用彩色视点中RGB三通道之间的相关性,对左右视点分别进行张量分解。每一个视点分解出三个特征图,其中每个视点的第一特征图保留了每个视点中较强的三通道关系。其次,结合左右视点之间的相关性,联合左右视点的第一特征图进行张量分解,得到立体图像的主要能量特征图。最后,将主要能量特征图进行奇异值分解,并嵌入水印从而提高鲁棒性。实验结果表明,该方法具有较高的鲁棒性和不可见性,并且实现了水印的盲提取。

  • Overview: With the development of network and multimedia technology, two dimensional (2D) image or video has become ubiquitous in all aspects of people's life, and real-time 2D video visual communication has matured. However, the 2D video cannot meet the visual requirements of people. The 3D realism has gradually become a fashion pursuit, since 3D images or videos provide the depth information of the scene and enhance the sense of reality. The demand for the development and applications of the 3D video system in the market is becoming much more urgent. It has a wide range of applications prospects in many fields such as stereoscopic digital television, distance education, 3D video conference system, virtual reality system, telemedicine, etc. With the popularity of 3D videos or images, 3D multimedia content may be transmitted through the Internet and other non-secure channels, and they may be copied, tampered or illegally used. It is essential to prevent illegal dissemination of 3D multimedia information, and watermarking as embedding the secret data into the 3D images is the main technology of protecting copyright. So far, the robust watermarking method for the monocular image is almost mature, but only a few stereo image robust watermarking methods are reported. As shown in Fig, to solve the copyright protection of stereo images, a robust stereo image watermarking method based on correlations of left and right views is proposed. Because Tucker decomposition can preserve the main energy of the image well, it is performed on left and right views to make full use of the correlations of three channels in the color view. Each view is decomposed into three feature images, where the first feature map retains relationships of three channels in each view. Secondly, considering correlations between the left and right views, the first feature images of the left and right views are combined to be performed by using Tucker decomposition, and the main energy features images of the stereo image are obtained. Finally, the main energy feature image is decomposed by singular value decomposition, and watermark is embedded for the purpose of improving the robustness. The experimental results show that when different kinds of stereo images are attacked by Convolution Filter, JPEG compression, Median Filter, Scaling and Cropping, watermark can be extracted blindly, and recognized with high NC. Compared with the monocular image watermarking methods, the proposed algorithm is more robust.

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  • 图 1  三阶张量分解示意图

    Figure 1.  Third-order tensor decomposition diagram

    图 2  图像Baby张量分解后的三个特征图。(a)第一特征图; (b)第二特征图; (c)第三特征图

    Figure 2.  Three feature images of Baby after Tucker decomposition. (a) The first feature image; (b) The second feature image; (c) The third feature image

    图 3  基于左右视点相关性的立体图像鲁棒水印方法框图。(a)水印嵌入过程;(b)水印提取过程

    Figure 3.  Block diagrams of robust stereo images watermarking based on correlations of left and right views. (a) Watermark embedding process; (b) Watermark extraction process

    图 4  原始左右视点。(a) Baby;(b) Bowling;(c) Art;(d) Dolls

    Figure 4.  Original stereoscopic images. (a) Baby; (b) Bowling; (c) Art; (d) Dolls

    图 5  含水印的左右视点。(a) Baby;(b) Bowling;(c) Art;(d) Dolls

    Figure 5.  Watermarked stereoscopic images. (a) Baby; (b) Bowling; (c) Art; (d) Dolls

    图 6  原始水印

    Figure 6.  Original watermark

    图 7  击后提取的水印图像。(a) Conv1(NC=0.9249); (b) Conv2(NC=0.9655);(c) JPEG90(NC=0.9884); (d) Scaling0.9(NC=0.9271); (e) Scaling2(NC=0.9781); (f) Cropping0.95(NC=0.9911)

    Figure 7.  Extracted watermark image after attack. (a) Conv1(NC=0.9249); (b) Conv2(NC=0.9655); (c) JPEG90(NC=0.9884); (d) Scaling0.9(NC=0.9271); (e) Scaling2(NC=0.9781); (f) Cropping0.95(NC=0.9911)

    表 1  各立体图像的PSNR和SSIM

    Table 1.  PSNR and SSIM of all stereoscopic image

    Image Proposed Method 1
    Left view Right view
    PSNR SSIM PSNR SSIM PSNR SSIM
    Baby1 42.12 1.0000 42.28 1.0000 42.20 1.0000
    Bowling1 43.81 1.0000 44.04 1.0000 43.99 1.0000
    Art 43.54 1.0000 43.66 0.9999 44.00 1.0000
    Dolls 41.43 1.0000 41.48 0.9999 41.49 1.0000
    下载: 导出CSV

    表 2  攻击参数

    Table 2.  Attack parameters

    Attack Parameter value
    Convolution filter Window size=3×3 Conv1:a=1/9, b=2/9, c=1/9, d=2/9, e=4/9, f=2/9, g=1/9, h=2/9, i=1/9 Conv2:a=0, b=-1/9, c=0, d=-1/9, e=5/9, f=-1/9, g=0, h=-1/9, i=0
    JPEG compression Quality factor: 50, 90
    Median filter Window size: 3×3, 5×5
    Scaling Ratio (on each side): 0.9, 1.1, 1.5, 2
    Cropping Retain ratio (on each side): 0.71, 0.78, 0.84, 0.9, 0.95
    下载: 导出CSV

    表 3  所有视点攻击后提取水印的NC

    Table 3.  NC of all views by different attacks

    Attack Baby Bowling Art Dolls
    Proposed Method 1 Proposed Method 1 Proposed Method 1 Proposed Method 1
    Conv1 0.9416 0.9320 0.9744 0.9695 0.9464 0.9451 0.9270 0.9214
    Conv2 0.9748 0.9711 0.9972 0.9955 0.9888 0.9803 0.9861 0.9809
    JPEG90 0.9693 0.9542 0.9548 0.9531 0.9612 0.9150 0.9839 0.9781
    JPEG50 0.7124 0.7268 0.7601 0.7737 0.7132 0.7224 0.7381 0.7748
    Median3×3 0.9776 0.9677 0.9929 0.9921 0.9764 0.9150 0.9586 0.9474
    Median5×5 0.9721 0.9642 0.9924 0.9929 0.9769 0.9725 0.9601 0.9515
    Scaling0.9 0.9461 0.9406 0.9634 0.9571 0.9461 0.9405 0.9341 0.9340
    Scaling1.1 0.9737 0.9723 0.9849 0.9810 0.9721 0.9711 0.9704 0.9646
    Scaling1.5 0.9766 0.9728 0.9862 0.9821 0.9745 0.9736 0.9723 0.9634
    Scaling2 0.9762 0.9727 0.9861 0.9822 0.9744 0.9729 0.9723 0.9629
    Cropping0.71 0.9261 0.9014 0.9253 0.8999 0.9215 0.8958 0.9230 0.8961
    Cropping0.78 0.9392 0.9227 0.9382 0.9210 0.9341 0.9187 0.9370 0.9187
    Cropping0.84 0.9550 0.9353 0.9548 0.9338 0.9502 0.9303 0.9526 0.9303
    Cropping0.9 0.9695 0.9594 0.9698 0.9575 0.9664 0.9566 0.9685 0.9568
    Cropping0.95 0.9842 0.9786 0.9835 0.9769 0.9800 0.9765 0.9831 0.9764
    Average 0.9455 0.9381 0.9576 0.9512 0.9455 0.9324 0.9445 0.9372
    下载: 导出CSV

    表 4  算法结果比较

    Table 4.  Comparison of algorithm result

    Attack Ref. [19] Proposed
    JPEG90 0.9766 0.9673
    Gaussian low pass3×3 0.9899 0.9929
    Median3×3 0.9132 0.9764
    Average3×3 0.9327 0.9666
    Cropping1/8 0.8766 0.9904
    Cropping1/4 0.7462 0.9955
    Cropping1/2 0.4909 0.9891
    下载: 导出CSV
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出版历程
收稿日期:  2018-01-28
修回日期:  2018-05-09
刊出日期:  2018-12-01

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