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Real Time Implementation of Integer DCT based Video Watermarking Architecture

机译:基于整数DCT的视频水印架构的实时实现

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With the recent development in multimedia communication network, data integrity and security of original content is the area of concern. Video is the one of the most popular object which is being shared easily throughout the media. Video watermarking is the current state of research to resolve the video ownership and authenticity related issues. There is a substantial amount of development in software based video watermarking from last few years. The prior works mainly focused on video watermarking that targeted for raw video where the watermark is embedded on the uncompressed video. At the present video capturing devices produce their output in one of the video compression standard. Software watermarking introduces a measurable quantity of delay between video capturing and watermark embedding process. Thus, software watermarking is not one of the ideal choices for real time watermark embedding. In the paper, a novel invisible and robust integer Discrete Cosine Transform (DCT) based video watermarking has been proposed. The proposed video watermarking is developed for real time watermark embedding and can easily be adapted as primary part of H. 264 encoder. The proposed algorithm has an essential part in form of integer DCT. Integer DCT is implemented with two different approaches, one is with fully pipeline architecture and the other is recursive architecture, for better speed and area optimization. The robustness of the algorithm has been improved against some video attacks with introducing the concept of scene change detection.
机译:随着多媒体通信网络的最新发展,原始内容的数据完整性和安全性成为人们关注的领域。视频是最受欢迎的对象之一,可以轻松地在所有媒体上共享。视频水印是解决视频所有权和真实性相关问题的当前研究状态。近几年来,基于软件的视频水印技术有了很大的发展。先前的工作主要集中于视频水印,其针对原始视频,其中水印嵌入未压缩的视频中。目前,视频捕获设备以视频压缩标准之一产生其输出。软件水印在视频捕获和水印嵌入过程之间引入了可测量的延迟量。因此,软件水印不是实时水印嵌入的理想选择之一。在本文中,提出了一种新颖的基于不可见且鲁棒的整数离散余弦变换(DCT)的视频水印。拟议的视频水印技术是为实时水印嵌入而开发的,可以轻松地用作H.264编码器的主要部分。所提出的算法具有整数DCT形式的重要部分。整数DCT通过两种不同的方法来实现,一种是采用完全流水线体系结构,另一种是递归体系结构,以实现更好的速度和面积优化。通过引入场景变化检测的概念,针对某些视频攻击提高了该算法的鲁棒性。

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