Everyday introduce Gaussian noise during the process of

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Everyday very huge
amount 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 a
technique that embeds a watermark, also known as information into images,
audios and videos with the aid of an algorithm. Watermark is information which
can be extracted later for authentication and identification purposes. The
problem of illegal distribution and handling of digital video is turning out to
become a major issue. This issue is solved by embedding copyright information
into bit streams of any video. In the existing system, DCT based Watermarking; image watermarking technique is used to add
a code in the digital images. This method operates in frequency domain
embedding a pseudo-random sequence of real numbers in a selected set of DCT
coefficient. It is done is such a way to ensure non-erasability of the image
watermarking. While it ensures non-erasability, it does introduce Gaussian
noise during the process of watermarking, the contrast and brightness of the
hidden image will be affected due to the watermarking of information and also the entire image consumes a lot of memory
after the entire watermarking procedure is done. In this paper, PCA (Principal Component Analysis) based Framelet Transform is combined
with local digital watermarking algorithm and digital watermarking algorithm
based SVD (Singular Value Decomposition)
is proposed. It describes the generation process of
the PCA FT SVD algorithm in detail and obtains a series of scale-invariant
feature points. A large amount of candidate feature points are selected to
obtain the neighborhood which can be used to embed the watermark. The
advantages of the proposed system are robustness against watermarking attacks,
imperceptibility, capacity and security.

Keywords: Framelet Transform, Frame-by-Frame, Principal Component Analysis, Video
Watermarking, Singular Value Decomposition, Robustness, Copyright Protection

Categories: Data


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