2016
DOI: 10.17529/jre.v12i2.3993
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Analisis dan Simulasi Video Watermarking Menggunakan Metode Dual Tree Complex Wavelet Transform (DT-CWT) dan Singular Value Decomposition (SVD)

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Cited by 2 publications
(4 citation statements)
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“…In the system-based transform domain watermarking approach, a host frame in a video sequence is transformed into a new domain before the watermark is embedded [20]. The most widely used transforms include fast Fourier transform (FFT) [21], discrete cosine transform (DCT) [22], discrete wavelet transform (DWT), dual-tree complex wavelet transform (DTCWT) and singular value decomposition (SVD) [23,24]. Each of these transformations, which are used to simulate video frames in multiple ways, have unique characteristics.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In the system-based transform domain watermarking approach, a host frame in a video sequence is transformed into a new domain before the watermark is embedded [20]. The most widely used transforms include fast Fourier transform (FFT) [21], discrete cosine transform (DCT) [22], discrete wavelet transform (DWT), dual-tree complex wavelet transform (DTCWT) and singular value decomposition (SVD) [23,24]. Each of these transformations, which are used to simulate video frames in multiple ways, have unique characteristics.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Watermarking is a derivative of steganography, which has resistance to attacks (hackers), even though the inserted data is visible in the document that using by other parties. A watermark (watermark) is data that attached to a digital document, whether video, audio, text, where its presence does not affect the further processing of the document [2][8] [9]. This watermarking program works by inserting a watermark image into the original image to get an image containing a watermark.…”
Section: Watermarkmentioning
confidence: 99%
“…For example, there is a matrix A with m rows and n columns, and the rank r≤n≤m.. Matrix A can be decomposed into 3 matrices, that is, A=USVT. The analogy of the number of rows and columns of the U, S, and VT matrices [2]. Matrix U is an orthogonal matrix mxm : U=[u1,u2,...ur+1,...,um]; Vector ui, for i = 1,2,...,m comes from the orthogonal set…”
Section: Watermarkmentioning
confidence: 99%
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