Compression Technique Using DCT & Fractal Compression: A Survey

Steganography differs from digital watermarking because both the information and the very existence of the information are hidden. In the beginning, the fractal image compression method is used to compress the secret image, and then we encrypt this compressed data by DES.The Existing Steganographic approaches are unable to handle the Subterfuge attack i.e, they cannot deal with the opponents not only detects a message ,but also render it useless, or even worse, modify it to opponent favor. The advantage of BCBS is the decoding can be operated without access to the cover image and it also detects if the message has been tampered without using any extra error correction. To improve the imperceptibility of the BCBS, DCT is used in combination to transfer stego-image from spatial domain to the frequency domain. The hiding capacity of the information is improved by introducing Fractal Compression and the security is enhanced using by encrypting stego-image using DES.  Copyright © www.iiste.org Keywords: Steganography, data hiding, fractal image compression, DCT

Fractal image compression and the self-affinity assumption : a stochastic signal modelling perspective

Bibliography: p. 208-225.Fractal image compression is a comparatively new technique which has gained considerable attention in the popular technical press, and more recently in the research literature. The most significant advantages claimed are high reconstruction quality at low coding rates, rapid decoding, and "resolution independence" in the sense that an encoded image may be decoded at a higher resolution than the original. While many of the claims published in the popular technical press are clearly extravagant, it appears from the rapidly growing body of published research that fractal image compression is capable of performance comparable with that of other techniques enjoying the benefit of a considerably more robust theoretical foundation. . So called because of the similarities between the form of image representation and a mechanism widely used in generating deterministic fractal images, fractal compression represents an image by the parameters of a set of affine transforms on image blocks under which the image is approximately invariant. Although the conditions imposed on these transforms may be shown to be sufficient to guarantee that an approximation of the original image can be reconstructed, there is no obvious theoretical reason to expect this to represent an efficient representation for image coding purposes. The usual analogy with vector quantisation, in which each image is considered to be represented in terms of code vectors extracted from the image itself is instructive, but transforms the fundamental problem into one of understanding why this construction results in an efficient codebook. The signal property required for such a codebook to be effective, termed "self-affinity", is poorly understood. A stochastic signal model based examination of this property is the primary contribution of this dissertation. The most significant findings (subject to some important restrictions} are that "self-affinity" is not a natural consequence of common statistical assumptions but requires particular conditions which are inadequately characterised by second order statistics, and that "natural" images are only marginally "self-affine", to the extent that fractal image compression is effective, but not more so than comparable standard vector quantisation techniques

Significant medical image compression techniques: a review

Telemedicine applications allow the patient and doctor to communicate with each other through network services. Several medical image compression techniques have been suggested by researchers in the past years. This review paper offers a comparison of the algorithms and the performance by analysing three factors that influence the choice of compression algorithm, which are image quality, compression ratio, and compression speed. The results of previous research have shown that there is a need for effective algorithms for medical imaging without data loss, which is why the lossless compression process is used to compress medical records. Lossless compression, however, has minimal compression ratio efficiency. The way to get the optimum compression ratio is by segmentation of the image into region of interest (ROI) and non-ROI zones, where the power and time needed can be minimised due to the smaller scale. Recently, several researchers have been attempting to create hybrid compression algorithms by integrating different compression techniques to increase the efficiency of compression algorithms

Implementation of fractal image coding

The goal of this project is to implement a digital image encoder and decoder using a Fractal Block Coding compression algorithm for grayscale images, and to compare its performance to currendy popular algorithms such as JPEG. The algorithm used here is based on the published papers [1] - [3] ofA E. Jacquin, and in part, a paper [4] by B. Ramamurthi and A. Gersho. As stated in the project proposal, this algorithm has been simplified to enable the timely completion of the project

Enhanced Fractal Image Coding (FIC) with Collage and Reconstruction Residuals

In this paper, two new paradigms are proposed with fractal collage and reconstruction residuals to enhance FIC. In the first new paradigm, FIC is optimized using the reconstruction residuals. In the second paradigm, the selected collage residuals are used to correct the iterated function system (IFS) of FIC, and an effective technique for coding the selected collage residuals is applied based on DCT and embedded bit-plane coding. In the first paradigm, the reconstruction quality is improved without increasing the bit rate. Using the second paradigm, we can improve the reconstruction quality with a little bit (about 0.01 bpp) increase in bit rate. Experimental results show that the proposed paradigms achieve better performance than JPEG at lower bit rate and similar performance at higher bit rate

A Multi-Level Enhanced Color Image Compression Algorithm using SVD & DCT

Nowadays, computer technology is mostly concerned with storage capacity and performance. Compression of digital images has become a fundamental aspect of their transmission and storage. Due to storage and bandwidth constraints, it has become necessary to compress images before to transmission and storage. Not only can image compression techniques help to reduce storage space requirements, but they also aid increase transmission bandwidth. Color images are in trend these days during communication. Most of the researchers have worked only on grayscale images. This research proposes a hybrid approach that encompasses two cutting-edge picture compression algorithms: DCT & SVD. This research involves the advantages and strength of two cutting-edge picture compression algorithms that enable us to compress the color images without additional cost in computation, space and time. Here in this research, for experimental purposes, seam carving image dataset is used. The proposed method's performance is evaluated using the performance evaluation matrices, i.e., Size after Compression, MSE, PSNR, Normalized Co-relation (NC), Percentage Space-Saving, and Compression Ratio. The proposed method performance is also correlated with the two latest image compression techniques, i.e., DCT Block Truncation (DCTBT) and Discrete Cosine Transform - Vector Quantization (DCT-VQ). The findings show that the suggested hybrid color image compression approach is superior to existing compression according to different performance metrics

A Novel Approach for Compressing Surveillance System Videos

The surveillance systems are expected to record the videos in 24/7 and obviously it requires a huge storage space. Even though the hard disks are cheaper today, the number of CCTV cameras is also vertically increasing in order to boost up security. The video compression techniques is the only better option to reduce required the storage space; however, the existing video compression techniques are not adequate at all for the modern digital surveillance system monitoring as they require huge video streams. In this paper, a novel video compression technique is presented with a critical analysis of the experimental results

Enhanced SVD Based Image Compression Technique

With the growth of technology and entrance into the Digital World, it has found itself surrounded by a massive quantity of data. Dealing with such huge data/information will often creates difficulties while transmission of data or storage of data. One feasible solution to overcome such difficulties is to use a data compression technique. Image compression is a method in which the storage space or processing space of image is reduced without degrading the image standard or quality. It conjointly reduces the time needed for images to be uploaded over the Internet or downloaded from Internet. JPEG is a necessary technique used for image compression. So, in order to improve the quality of the image, compression is done using different techniques. In this research work, SVD algorithm is used for compression which is giving better result for image compression without any reduction in quality. The modeling of optimized Singular Value Decomposition (SVD) implemented for JPEG Image compression in MATLAB is implemented. SVD is the core part of the JPEG image compression. In JPEG Image Compression, a quantizer follows the SVD. Such structural channel is beneficial for reducing difficulty in the whole JPEG compression/encoding. To overcome the problem of  lossy compression implemented algorithm is designed in order to enhance the performance of compression algorithm with respect to performance evaluation parameters such as, Compression ratio , Bits per pixel , Peak signal to noise ratio, Mean squared error  and Signal to noise ratio

Self-similarity and wavelet forms for the compression of still image and video data

This thesis is concerned with the methods used to reduce the data volume required to represent still images and video sequences. The number of disparate still image and video coding methods increases almost daily. Recently, two new strategies have emerged and have stimulated widespread research. These are the fractal method and the wavelet transform. In this thesis, it will be argued that the two methods share a common principle: that of self-similarity. The two will be related concretely via an image coding algorithm which combines the two, normally disparate, strategies. The wavelet transform is an orientation selective transform. It will be shown that the selectivity of the conventional transform is not sufficient to allow exploitation of self-similarity while keeping computational cost low. To address this, a new wavelet transform is presented which allows for greater orientation selectivity, while maintaining the orthogonality and data volume of the conventional wavelet transform. Many designs for vector quantizers have been published recently and another is added to the gamut by this work. The tree structured vector quantizer presented here is on-line and self structuring, requiring no distinct training phase. Combining these into a still image data compression system produces results which are among the best that have been published to date. An extension of the two dimensional wavelet transform to encompass the time dimension is straightforward and this work attempts to extrapolate some of its properties into three dimensions. The vector quantizer is then applied to three dimensional image data to produce a video coding system which, while not optimal, produces very encouraging results