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Video Watermarking Algorithm Based on NSCT, Pseudo 3D-DCT and NMF.
Fan, Di; Zhang, Xiao; Kang, Wenshuo; Zhao, Huiyuan; Lv, Yingjun.
  • Fan D; College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
  • Zhang X; College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
  • Kang W; College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
  • Zhao H; College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
  • Lv Y; Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan 250031, China.
Sensors (Basel) ; 22(13)2022 Jun 23.
Article en En | MEDLINE | ID: mdl-35808245
Video watermarking is an important means of video and multimedia copyright protection, but the current watermarking algorithm is difficult to ensure high robustness under various attacks. In this paper, a video watermarking algorithm based on NSCT, pseudo 3D-DCT and NMF has been proposed. Combined with NSCT, 3D-DCT and NMF, the algorithm embeds the encrypted QR code copyright watermark into the NMF base matrix to improve the anti-attack ability of the watermark under the condition of invisibility. The experimental results show that the algorithm ensures the invisibility of the watermark with a high signal-to-noise ratio of the video, and meanwhile has high ability and robustness against common single and combined attacks, such as filtering, noise, compression, shear, rotation and so on. The issue that the video watermarking algorithm has poor resistance to various attacks, especially the shearing attack, has been solved in this paper; thus, it can be used for digital multimedia video copyright protection.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2022 Tipo del documento: Article