LOSSLESS AND LOSSY IMAGE COMPRESSION BASED ON DATA FOLDING

Image compression plays a very important role in image processing especially when we have to send the image on the internet. Since imaging techniques produce prohibitive amounts of data, compression is necessary for storage and communication purposes. Many current compression schemes provide a very high compression rates but with considerable loss of quality. On the other hand, in some areas in medicine, it may be sufficient to maintain high image quality only in the region of interest, i.e., in diagnostically important regions called region of interest. In the proposed work images are compressed using Data folding technique which uses the property of adjacent neighbour redundancy for prediction. In this method first column folding is applied followed by the row folding iteratively till the image size reduces to predefined value, then arithmetic encoding is applied which results the compressed image at the end before transmitting the data. In this paper lossless compression is achieved only at the region of interest and it is mainly suitable for medical images

On the Application of Dictionary Learning to Image Compression

Signal models are a cornerstone of contemporary signal and image-processing methodology. In this chapter, a particular signal modelling method, called synthesis sparse representation, is studied which has been proven to be effective for many signals, such as natural images, and successfully used in a wide range of applications. In this kind of signal modelling, the signal is represented with respect to dictionary. The dictionary choice plays an important role on the success of the entire model. One main discipline of dictionary designing is based on a machine learning methodology which provides a simple and expressive structure for designing adaptable and efficient dictionaries. This chapter focuses on direct application of the sparse representation, i.e. image compression. Two image codec based on adaptive sparse representation over a trained dictionary are introduced. Experimental results show that the presented methods outperform the existing image coding standards, such as JPEG and JPEG2000

Image Compression Techniques Comparative Analysis using SVD-WDR and SVD-WDR with Principal Component Analysis

The image processing is the technique which can process the digital information stored in the form of pixels. The image compression is the technique which can reduce size of the image without compromising quality of the image. The image compression techniques can classified into lossy and loss-less. In this research work, the technique is proposed which is SVD-WDR with PCA for lossy image compression. The PCA algorithm is applied which will select the extracted pixels from the image. The simulation of proposed technique is done in MATLAB and it has been analyzed that it performs well in terms of various parameters. The proposed and existing algorithms are implemented in MATLAB and it is been analyzed that proposed technique performs well in term of PSNR, MSE, SSIM and compression rate. In proposed technique the image is firstly compressed by WDR technique and then wavelet transform is applied on it. After extracting features with wavelet transform the patches are created and patches are sorted in order to perform compression by using decision tree. Decision tree sort the patches according to NRL order that means it define root node which maximum weight, left node which has less weight than root node and right node which has minimum weight. In this way the patches are sorted in descending order in terms of its weight (information). Now we can see the leaf nodes have the least amount of information (weight). In order to achieve compression of the image the leaf nodes which have least amount of information are discarded to reconstruct the image. Then inverse wavelet transform is applied to decompress the image. When the PCA technique is applied decision tree classifier the features which are not required are removed from the image in the efficient manner and increase compression ratio

Gbit/second lossless data compression hardware

This thesis investigates how to improve the performance of lossless data compression hardware as a tool to reduce the cost per bit stored in a computer system or transmitted over a communication network. Lossless data compression allows the exact reconstruction of the original data after decompression. Its deployment in some high-bandwidth applications has been hampered due to performance limitations in the compressing hardware that needs to match the performance of the original system to avoid becoming a bottleneck. Advancing the area of lossless data compression hardware, hence, offers a valid motivation with the potential of doubling the performance of the system that incorporates it with minimum investment. This work starts by presenting an analysis of current compression methods with the objective of identifying the factors that limit performance and also the factors that increase it. [Continues.

Sparse representation based hyperspectral image compression and classification

Abstract This thesis presents a research work on applying sparse representation to lossy hyperspectral image compression and hyperspectral image classification. The proposed lossy hyperspectral image compression framework introduces two types of dictionaries distinguished by the terms sparse representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively. The former is learnt in the spectral domain to exploit the spectral correlations, and the latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in hyperspectral images. To alleviate the computational demand of dictionary learning, either a base dictionary trained offline or an update of the base dictionary is employed in the compression framework. The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of both SRSD and MSSD approaches. For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation. This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression. Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification.EM201

Exclusive-or preprocessing and dictionary coding of continuous-tone images.

The field of lossless image compression studies the various ways to represent image data in the most compact and efficient manner possible that also allows the image to be reproduced without any loss. One of the most efficient strategies used in lossless compression is to introduce entropy reduction through decorrelation. This study focuses on using the exclusive-or logic operator in a decorrelation filter as the preprocessing phase of lossless image compression of continuous-tone images. The exclusive-or logic operator is simply and reversibly applied to continuous-tone images for the purpose of extracting differences between neighboring pixels. Implementation of the exclusive-or operator also does not introduce data expansion. Traditional as well as innovative prediction methods are included for the creation of inputs for the exclusive-or logic based decorrelation filter. The results of the filter are then encoded by a variation of the Lempel-Ziv-Welch dictionary coder. Dictionary coding is selected for the coding phase of the algorithm because it does not require the storage of code tables or probabilities and because it is lower in complexity than other popular options such as Huffman or Arithmetic coding. The first modification of the Lempel-Ziv-Welch dictionary coder is that image data can be read in a sequence that is linear, 2-dimensional, or an adaptive combination of both. The second modification of the dictionary coder is that the coder can instead include multiple, dynamically chosen dictionaries. Experiments indicate that the exclusive-or operator based decorrelation filter when combined with a modified Lempel-Ziv-Welch dictionary coder provides compression comparable to algorithms that represent the current standard in lossless compression. The proposed algorithm provides compression performance that is below the Context-Based, Adaptive, Lossless Image Compression (CALIC) algorithm by 23%, below the Low Complexity Lossless Compression for Images (LOCO-I) algorithm by 19%, and below the Portable Network Graphics implementation of the Deflate algorithm by 7%, but above the Zip implementation of the Deflate algorithm by 24%. The proposed algorithm uses the exclusive-or operator in the modeling phase and uses modified Lempel-Ziv-Welch dictionary coding in the coding phase to form a low complexity, reversible, and dynamic method of lossless image compression

Lossless data compression and decompression algorithm and its hardware architecture

LZW (Lempel Ziv Welch) and AH (Adaptive Huffman) algorithms were most widely used for lossless data compression. But both of these algorithms take more memory for hardware implementation. The thesis basically discuss about the design of the two-stage hardware architecture with Parallel dictionary LZW algorithm first and Adaptive Huffman algorithm in the next stage. In this architecture, an ordered list instead of the tree based structure is used in the AH algorithm for speeding up the compression data rate. The resulting architecture shows that it not only outperforms the AH algorithm at the cost of only one-fourth the hardware resource but it is also competitive to the performance of LZW algorithm (compress). In addition, both compression and decompression rates of the proposed architecture are greater than those of the AH algorithm even in the case realized by software.Three different schemes of adaptive Huffman algorithm are designed called AHAT, AHFB and AHDB algorithm. Compression ratios are calculated and results are compared with Adaptive Huffman algorithm which is implemented in C language. AHDB algorithm gives good performance compared to AHAT and AHFB algorithms. The performance of the PDLZW algorithm is enhanced by incorporating it with the AH algorithm. The two stage algorithm is discussed to increase compression ratio with PDLZW algorithm in first stage and AHDB in second stage. Results are compared with LZW (compress) and AH algorithm. The percentage of data compression increases more than 5% by cascading with adaptive algorithm, which implies that one can use a smaller dictionary size in the PDLZW algorithm if the memory size is limited and then use the AH algorithm as the second stage to compensate the loss of the percentage of data reduction. The Proposed two–stage compression/decompression processors have been coded using Verilog HDL language, simulated in Xilinx ISE 9.1 and synthesized by Synopsys using design vision