Everyday introduce Gaussian noise during the process of

Everyday very hugeamount of data is embedded on digital media or distributed over the internet.The data is so distributed that it can be replicated easily without any error.

Even with encryption technique, when distributed it can be decrypted and copied.One way to avoid this is to make use of digital watermarking technique. It is atechnique that embeds a watermark, also known as information into images,audios and videos with the aid of an algorithm. Watermark is information whichcan be extracted later for authentication and identification purposes. Theproblem of illegal distribution and handling of digital video is turning out tobecome a major issue.

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This issue is solved by embedding copyright informationinto bit streams of any video. In the existing system, DCT based Watermarking; image watermarking technique is used to adda code in the digital images. This method operates in frequency domainembedding a pseudo-random sequence of real numbers in a selected set of DCTcoefficient. It is done is such a way to ensure non-erasability of the imagewatermarking. While it ensures non-erasability, it does introduce Gaussiannoise during the process of watermarking, the contrast and brightness of thehidden image will be affected due to the watermarking of information and also the entire image consumes a lot of memoryafter the entire watermarking procedure is done. In this paper, PCA (Principal Component Analysis) based Framelet Transform is combinedwith local digital watermarking algorithm and digital watermarking algorithmbased SVD (Singular Value Decomposition)is proposed. It describes the generation process ofthe PCA FT SVD algorithm in detail and obtains a series of scale-invariantfeature points.

A large amount of candidate feature points are selected toobtain the neighborhood which can be used to embed the watermark. Theadvantages of the proposed system are robustness against watermarking attacks,imperceptibility, capacity and security. Keywords: Framelet Transform, Frame-by-Frame, Principal Component Analysis, VideoWatermarking, Singular Value Decomposition, Robustness, Copyright Protection


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