视频可逆数据隐藏:向局部失真容限框架演进

IF 3.4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Signal Processing Pub Date : 2024-10-11 DOI:10.1016/j.sigpro.2024.109730
Jiaqi Wang, Bo Ou
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引用次数: 0

摘要

近年来,用于视频版权保护的可逆数据隐藏(RDH)技术迅速发展,引起了学术界更多的关注。本文提出了一种局部失真容差视频 RDH 方法,在多个区块上实现补偿嵌入,以提高嵌入效率。具体来说,失真漂移的影响是在局部区域而不是单个块中校准的,通过减少区域内的失真漂移来提高性能。失真容限向量用于对局部区域的区块进行排序,独立于相邻区域的区块将有更高的机会嵌入秘密比特。然后,以成对的方式修改系数。由于一对系数中只有一个系数用于嵌入,因此可以对称地修改另一个系数进行补偿。实验结果验证了所提方法在减少帧内失真漂移、增加容量和最小化比特率增加方面的有效性。
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Video reversible data hiding: An evolution to local distortion-tolerance framework
Recently, the rapid development of reversible data hiding (RDH) for video copyright protection has attracted more attentions of academic community. In this paper, a local distortion-tolerance video RDH method is proposed to achieve a compensatory embedding on multiple blocks for a higher embedding efficiency. Specifically, the effect of distortion drift is calibrated in a local region rather than in the single block, and the performance enhancement is obtained by the reduction of distortion drift over the region. The distortion-tolerance vector is used to rank the blocks in the local region and the blocks being independent of the adjacent regions will have higher chance to be embedded with secret bits. Then, the coefficients are modified in a pairwise manner. Since only one coefficient in a pair is used for embedding, the other one can be modified symmetrically for compensation. The experimental results validate the effectiveness of the proposed method to decrease the intra-frame distortion drift, increase the capacity and minimize the bit rate increase.
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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