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An automatic scheme for stereoscopic video object-based watermarking using qualified significant wavelet tress

机译:基于立体视频对象的自动化方案使用合格的重要小波杆的基于立体视频对象的水印

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In this paper a fully automatic system for embedding visually recognizable watermark patterns to video objects is proposed. The architecture consists of 3 main modules. During the first module unsupervised video object extraction is performed into three levels with ten subbands, using the Discrete Wavelet Transform (DWD and three pairs of subbands are formed (HL{sub}3, HL{sub}2, (LH{sub}3, LH{sub}2) and (HH{sub}3, HH{sub}2). Next Qualified Significant Wavelet Trees (QSWTs) are estimated for the specific pair of subbands that contains the highest energy content compared to the other two pairs. QSWTs are derived from the Embedded Zerotree Wavelet (EZW) algorithm and they are high-energy coefficient paths within the selected pair of subbands. Finally during the third module, visually recognizable watermark patterns are redundantly embedded to the coefficients of the highest energy QSWTs and the inverse DWT is applied to provide the watermarked video object. The performance of the proposed video object watermarking system is tested under various signal distortions such as JPEG lossy compression, sharpening, blurring and adding different types of presented to indicate the efficiency and robustness of the proposed noise. Experimental results on real life stereoscopic images are scheme.
机译:在本文中,提出了一种用于将视觉上可识别的水印模式嵌入到视频对象的全自动系统。该架构由3个主模块组成。在第一模块期间,无监督的视频对象提取被执行到具有十个子带的三个级别,使用离散小波变换(DWD和三对子带形成(HL {Sub} 3,HL {Sub} 2,(LH {Sub} 3 ,LH {子} 2)和(HH {子} 3,HH {子} 2)。接下来合格重大小波树(QSWTs)估计针对特定对的子带包含最高能量含量相比于其它两对。QSWTS来自嵌入的Zerotree小波(EZW)算法,它们是所选子带内的高能系数路径。最后在第三模块期间,视觉上可识别的水印模式被冗余地嵌入到最高能量Qswts的系数上和逆DWT应用于提供水印视频对象。在各种信号失真下测试了所提出的视频对象水印系统的性能,例如JPEG损耗压缩,锐化,模糊和添加不同类型的呈现表示提出噪声的效率和稳健性。实验结果对实际立体图像是方案。

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