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.

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

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:

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.