Wednesday, 26 April 2017

Image Watermarking Using Singular Value Decomposition and Discrete Wavelet Transform

In recent times, internet is being increasingly used as the platform for distribution of digital multimedia content. The inherent flexibility of Internet facilitates users to transact with one another to create, distribute, store, peruse, subscribe, enhance, modify and trade digital content in various forms like text documents, databases, e-books, still images, audio, video, computer software and games.

The use of an open medium like Internet gives rise to concerns about protection and enforcement of intellectual property rights of the digital content involved in the transaction. In addition, unauthorized replication and manipulation of digital content is relatively trivial and can be done using inexpensive tools, unlike the traditional analog multimedia content. The protection and enforcement of intellectual property rights for digital media has become an important issue. In recent years, the research community has seen much activity in the area of digital watermarking as an additional tool in protecting digital content.

Watermarking is descendent of steganography which has been in existence for at least a few hundred years. Watermarking is a special technique of steganography where one message is embedded in another and the two messages are related to each other in some way. The most common examples of watermarking are the presence of specific patterns in currency notes which are visible only when the note is held to light and logos in the background of printed text documents. The watermarking techniques prevent forgery and unauthorized replication of physical objects. 

To provide a robust watermark, a good strategy is to embed the watermark signal into the significant portion of the host signal. This portion of the host data is highly sensitive to alterations, however, and may produce very audible or visible distortions in the host data. Applications for digital watermarking include copyright protection, fingerprinting, authentication, copy control, tamper detection, and data hiding applications such as broadcast monitoring. Watermarking algorithms have been developed for audio, still images, video, graphics, and text.

                Click here to Download the MATLAB Code

Wednesday, 5 April 2017

Convert RGB Image to Gray Scale Image to Binary Image Using Threshold Technique


RGB image is a 3D image, it is also called as true color image, the data is stored in the form of x by y by 3  data array. The three data array define red , green and blue color value for every pixel. Red, Green and Blue color components uses 8 bits each so in total there are 24 bits, it implies (2^24) 16 million colors.


Gray Scale Image

These are the 2D images with a single 2D data matrix.






Tuesday, 4 April 2017

Linear Convolution of Two Discrete Time Sequences

The convolution gives the response y(n)  of the Linear Time Invariant System as a function of the input signal x(n) and the impulse response h(n).

Definition of the Linear convolution is :




The process can be summarized as follows:


Folding: Fold h(k) about k = 0 to obtain h(-k).

Shifting: Shift h(-k) by p to the right (left)  if p is positive (negative), to obtain h(p-k).

Multiplication: Multiply x(k) by  h(p-k) to obtain the product sequences as x(k)h(p-k).

Summation: sum all the values of the product sequences to obtain the values of the output.








Discrete Fourier Transform

Frequency analysis of discrete time signals is usually and most conveniently performed on a digital signal processor, which may be a general purpose digital computer or specially designed digital hardware. To perform frequency analysis on a discrete time signal {x(n)}, we convert the time domain sequence to an equivalent frequency domain representation. We know that such a representation is given by the Fourier transform X(w) of the sequence {x(n)}. However X(w) is a continuous function of frequency and therefore, it is not a computationally convenient representation of the sequence  {x(n)}.

Definition of Discrete Fourier Transform


When the Sequence x(n) has a finite length L, less than or equal to N than the discrete Fourier transform can be written as :

 Where k = 0,1,2,...,N - 1

Inverse Discrete Fourier Transform
  Where n = 0,1,2,...,N - 1


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