Wednesday, 24 December 2014

MATLAB code for High capacity steganography for binary image and text.


This code embeds one and more than one bits in single pixel of color image. Secrete image and secrete message at a time embedded into color cover image. 
Cover Image: 
Given cover image is a Color image
Let 'A' is an original image having size 'm*n*p' represented as

Sunday, 23 November 2014

MATLAB code for DCT based Gray-scale Image Compression.

Number of bits required to represent the information in an image can be minimized by removing the redundancy present in it
There are three types of redundancies:
1. Spatial redundancy
Which is due to the correlation or dependence between neighboring pixel values;
2. Spectral redundancy,
Which is due to the correlation between different color planes or spectral bands;

Monday, 29 September 2014

MATLAB code for DCT based Color Image Compression.

Number of bits required to represent the information in an image can be minimized by removing the redundancy present in it
There are three types of redundancies:
1. Spatial redundancy
Which is due to the correlation or dependence between neighboring pixel values;
2. Spectral redundancy,
Which is due to the correlation between different color planes or spectral bands;

Thursday, 19 June 2014

How to apply Average filter, Weighted filter and Median Filter to Noisy Image?


Some neighborhood operations work with the values of the image pixels in the neighborhood and the corresponding values of a sub image that has the same dimensions as the neighborhood. The sub image is called a filter, mask, kernel, template, or window, with the first three terms being the most prevalent terminology. The values in a filter sub image are referred to as coefficients, rather than pixels. The process consists simply of moving the filter mask from point to point in an image. At each point (x, y), the response of the filter at that point is calculated using a predefined relationship.

Thursday, 12 June 2014

MATLAB code of Analysis of LSB Based Steganography.


INTRODUCTION:
Information hiding in digital images has drawn much attention in recent years. Secret message encrypted and embedded in digital cover media. The redundancy of digital media, as well as characteristics of the human visual system, makes it possible to hide secret messages. Two competing aspects are considered while designing information hiding scheme
1) Hiding capacity and
2) Imperceptibility.

Tuesday, 10 June 2014

MATLAB code for Iris Recognition system (DCT Based).

INTRODUCTION:
A biometric system provides automatic recognition of an individual based on some sort of unique feature or characteristic possessed by the individual. Biometric systems have been developed based on fingerprints, facial features, voice, hand geometry, handwriting, the retina, and the one presented in this thesis, the iris. Biometric systems work by first capturing a sample of the feature, such as recording a digital sound signal for voice recognition, or taking a digital color image for face recognition.

Thursday, 29 May 2014

MATLAB Implementation of Image Fusion using PCA, Stationary and Discrete Wavelet Transform.


IMAGE FUSION:
Image Fusion is a process of combining the relevant information from a set of images of the same scene into a single image and the resultant fused image will be more informative and complete than any of the input images.
Input images could be multi sensor, multimodal, multi focus or multi temporal. There are some important requirements for the image fusion process:

Monday, 26 May 2014

MATLAB Implementation of Face Recognition using PCA and Eigen Face Approach.



Face is a complex multidimensional structure and needs a good computing techniques for recognition. Our approach treats face recognition as a two-dimensional recognition problem. In this scheme face recognition is done by Principal Component Analysis (PCA). Face images are projected onto a face space that encodes best variation among known face images. The face space is defined by Eigen face which is eigen vectors of the set of faces, which may not correspond to general facial features such as eyes, nose, and lips. The Eigen face approach uses the PCA for recognition of the images.

Wednesday, 5 February 2014

How to Calculate PSNR (Peak Signal to Noise Ratio) in MATLAB?

Peak-Signal to Noise Ratio (PSNR)
1. The PSNR is most commonly used as a measure of quality of reconstruction of lossy compression codec’s (e.g., for image compression).
2. The signal in this case is the original data, and the noise is the error introduced by compression.

Saturday, 11 January 2014

LSB Substitution Steganography MATLAB Implementation.

Basically there are main four mediums in which steganography is to be carried out. These four mediums are Text, Image, Audio/Video and Protocol. Image stegnography plays important role in stenographic field. Image stegnography is divided into Spatial domain and Transform domain. Spatial domain further divided into simple LSB (least significant bit) substitution, LSB matching and PVD (pixel value difference). Transform domain is one of most significant domain in image stegnography.

Friday, 10 January 2014

Comparison between SPIHT and Advanced SPIHT with Huffman coding.....Experimental Results.


Quality Parameters 
1. Mean Square Error
Two commonly used measures for quantifying the error between images are Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The MSE between two images I and K is denoted by

Thursday, 9 January 2014

MATLAB Implementation of Advanced SPIHT with Huffman coding.


We have Published MATLAB Implementation of SPIHT (Set Partitioning in Hierarchical Trees) in previous blog post. You can see this here. This consists of DWT, Quantization and SPIHT encoding. At the end of these processes we will get final compressed code stream.  

Advanced SPIHT with Huffman: 
Transmitter:
Transmitter:

Tuesday, 7 January 2014

MATLAB Implementation of SPIHT (Set Partitioning in Hierarchical Trees).


Traditional image coding technology mainly uses the statistical redundancy between pixels to reach the goal of compressing. The research on wavelet transform image coding technology has made a rapid progress. Because of its high speed, low memory requirements and complete reversibility, digital wavelet transform (IWT) has been adopted by new image coding standard, JPEG 2000. The embedded zero tree wavelet (EZW) algorithms have obtained not bad effect in low bit-rate image compression. Set Partitioning in Hierarchical Trees (SPIHT) is an improved version of EZW and has become the general standard of EZW.