摘要
现有自适应视频隐写的成本分配方法主要针对特定变换系数,导致容量较低。此外,失真漂移是HEVC(high efficiency video coding)视频隐写面临的一大挑战。因此,结合HEVC视频编码的帧内帧间过程,提出了一种代价分配方法,以实现高容量、低失真传递的高性能视频自适应隐写。首先,该方法针对HEVC视频编码中的离散正弦变换特征进行研究,分析了这些系数在受到扰动后所产生的误差传播规律。在嵌入过程中,对修改变换系数导致的块内失真、块间失真、帧间失真进行了详细分析,并考虑不同块隐写产生的块间失真差异对块进行分类。该算法充分利用所有的非零变换系数,为不同的载体系数分配了不同的失真代价,将隐秘信息嵌入到对视频质量影响较小的帧中。实验结果表明,与现有的HEVC视频系数域隐写方法相比,该算法在视频码率、视频质量和嵌入容量方面具有一定的优势。
Existing cost allocation methods for adaptive video steganography mainly focus on specific transform coefficients,resulting in lower capacity.Moreover,distortion drift is a significant challenge for steganography in HEVC videos.Therefore,this paper proposed a cost allocation method that combined the intra-frame and inter-frame processes of HEVC video coding to achieve high-capacity,low-distortion transmission in high-performance adaptive video steganography.Firstly,the method investigated the discrete sine transform features in HEVC video coding,analyzing the error propagation patterns of these coefficients under disturbance.During the embedding processed,it conducted a detailed analysis on the intra-block distortion,inter-block distortion,and inter-frame distortion caused by modifying transform coefficients.The algorithm also took into account the differentiation in inter-block distortion resulting from steganography in different blocks,leading to the classification of blocks.This paper maximized the utilization of all non-zero transform coefficients,allocating distinct distortion costs for various carrier coefficients.The covert information was then embedded into frames that minimally impacted video quality.Experimental results indicate that,compared to existing HEVC video coefficient domain steganography methods,the proposed algorithm demonstrates advantages in terms of video bitrate,video quality,and embedding capacity.
作者
朱燕彬
徐达文
Zhu Yanbin;Xu Dawen(School of Information Engineering,Chang’an University,Xi’an 710064,China;School of Cyber Science&Engineering,Ningbo University of Technology,Ningbo Zhejiang 315211,China)
出处
《计算机应用研究》
CSCD
北大核心
2024年第8期2508-2514,共7页
Application Research of Computers
基金
国家自然科学基金资助项目(62071267)
宁波市自然科学基金资助项目(2023J022)。