Wavelet-based Compression Technique Research Articles

Performance Evaluation of Symmetrical Encryption Algorithms with Wavelet Based Compression Technique

To overcome the different issues connected with security and limited bandwidth during transmission of images, compression and encryption play an important role. To measure the effect on the performance of compression technique followed by encryption techniques is a challenging task. In this paper, biorthogonal discrete wavelet transform (DWT) technique is proposed for compression which is followed by advanced encryption standard (AES) and data encryption standard (DES) technologies to achieve secure transmission of data. The performances of DWT-AES and DWT-DES Compression-encryption (CE) algorithms are analyzed based on statistical and differential parameters. We illustrate the authentication of results by applying the proposed CE algorithms over five standard test images and also by comparing with different state of the art methods. The results show that combination of DWT-DES CE methodology provides high quality of reconstructed image with robustness against different attacks.

A survey and evaluation of bior wavelet based compression techniques

A survey and evaluation of bior wavelet based compression techniques

Contourlet transform based subband normalization for region based medical image compression

Low bit rate compression approach has been proposed for easy transmission of medical image from one place to another. Contourlet transform technique is used for obtaining multi directional subbands and for capturing the fine details of the image. Transformed coefficients are then normalized using a mathematical approach for each subbands, followed by quantization and encoding. Arithmetic coding techniques are used for entropy encoding. Experimental results shows that the visual quality as well as the compression ratio of the reconstructed image is high compared to the existing wavelet based compression technique at low bit rates. Contextual compression is well supported for such conditions where the unwanted regions are compressed lossy, but with good visual quality. The result of the proposed method shows better compression performance with good visual quality at low bit rates.

Selective Coding for Multilevel Wavelet Image Compression

Objectives: The basic aim of this paper is to develop a coding method which will give effective compression by retaining image accuracy with lower computational overhead in image coding. The objective for obtaining such a coding technique is made by the development of an improvised preprocessing approach followed by a modified planar coding with the transformation made from wavelet to multi-wavelet. Methods/Statistical Analysis: This paper analyzes the performance of existing wavelet based compression techniques. Also develops a coding approach so as to obtain effective compression by retaining image accuracy with lower computational overhead in image coding. The overall implementation consists of improvised preprocessing followed by a modified planar coding with the transformation made from wavelet to multiwavelet approach. The main problems focused in this research work are 1. Preprocessing (Filtering), 2. Representing image coding coefficients and 3. Coding schemes for multi-Bit rate compatibility with minimum representation. For noise removal a modified weighted filtration approach is proposed. With the proposed approach an improvement in coding efficiency is achieved. The simulation observation evaluates the proposed approach and the comparative analysis of the proposed approach presents the improvements achieved. The performance of proposed selective MWVLT (S-MWLT) is compared with the conventional Multi Wavelet coding (MWVLT) and DWT based coding. The assessment is carried out by observations on various test samples. Findings: To test the operation performance for developed system the PSNR, RMSE for the system is evaluated under different medium distortion level. The observation illustrates that the obtained visualization of the filtered result using weighted filtration is comparatively more accurate than the conventional filtration approach while RMSE value for the proposed approach is decreased to about 40 units as comparative to the conventional approach. It is observed that the obtained filtration is improved with the block size increment. At N=4 the obtained filtration is comparatively accurate. Analysis of different wavelet transformations at variable bits per pixel is carried out. From analysis it is clear that Mean Square Error (MSE) and computational time is less in symplet transformation as well as high PSNR is obtained for symplet transformation. Analysis of DWT, MWVLT, S-MWVLT at different noise variance is carried out. The MSE value is observed minimum for the proposed S-MWLT coding than DWT and MWLT. This is due to minimal correlative band selection, the MSE of recovered sample is observed to be lower. While the compression achieved for the proposed S-MWVLT coding, is comparatively higher than the DWT based coding. The overhead is lower in case of DWT based, however in comparison to MWVLT coding; proposed S-MWVLT coding has lower overhead. The PSNR for the proposed approach is observed to be improved by a factor of 7 dB in comparison to MWVLT coding and about 10 dB in comparison to DWT based coding. Also at higher coding rate proposed S-MWVLT coding performs better than DWT, MWVLT. Application/Improvements: The image compression using selective Multiwavelet coding can be extended for video compression and can be applied in multimedia communication. It can also be extended for other image processing applications (may be face recognition, passport size image compression, etc.) for better results. This technique can also be combined with image security.

Prediction error based compression of color images using WDR coding

This work presents an efficient compression of color images using Wavelet Difference Reduction (WDR) coding and prediction error quantization. After reducing the correlation among RGB planes using RGB to YCbCr transform, each of the transformed planes are decomposed into several sub-images. The sub-images of Y plane are compressed independently without much more degrading the image quality, while each of the sub-images of Cb and Cr planes are compressed using quantization and WDR and Huffman coding techniques. At the receiver side, the original image content is recovered with the help of compressed data and quantizing parameters, used during compression. To give the evidence of our findings the proposed work is compared with several wavelet based compression techniques. The experimental results and analysis of the proposed work demonstrate its superiority both in terms of objective and subjective fidelity criteria.

Histogram Equilization on Wavelet based Compression Techniques

This paper analyzes the effect of histogram equalization on the wavelet based compression techniques. Image compression is reducing the size of an image using one of the encoding techniques. Encoding helps in saving disk space by representing the same data in lesser number of bytes. Histogram is a graphical representation of intensity distribution of an image. It expresses the number of pixel values for each intensity value of an image. Histogram Equalization is a method used to improve the contrast of an image to stretch out the full range of an intensity .Comparison among algorithms has been made with respect to PSNR (Peak signal to noise ratio), MSE (Mean square error), MAE (Mean Absolute Error), L2RAT, Compression ratio.

Palmprint Compression using Wavelet based Contourlet Transform and SPIHT

Wavelets are capable of approximating images with sharp discontinuities but not good at the approximation of images with smooth contours. Hence contourlet transform was introduced. It produces an efficient multi resolution and directional decomposition of an image using a laplacian pyramidal decomposition and a directional filter bank. It has good approximation capability for smooth 2D signals. A nonredundant version of this transform, Wavelet Based Contourlet Transform (WBCT) is appropriate for image compression. In this work, the use of WBCT for the compression of palmprint database and its performance are evaluated. The experimental results show that this coding is competitive to the wavelet based compression technique and provides visually superior quality of reconstruction for the mentioned images.

A Unified Scheme For Fast And Efficient Spiht Coding Algorithm Used For Image Compression

This paper concerns Image compression obtained with wavelet-based compression techniques such as setpartitioning in hierarchical trees (SPIHT)yield very good results The necessity in image compression continuously grows during the last decade, different types of methods is used for this mainly EZW, SPIHT and others. In this paper we used discrete wavelet transform and after this set-partitioning in hierarchical trees (SPIHT) with some improvement in respect of encoding and decoding time with better PSNR with respect to EZW coding.

Beta wavelet based ECG signal compression using lossless encoding with modified thresholding

In this paper, an ECG compression method based on beta wavelet using lossless encoding technique is presented. Wavelet based compression techniques minimize the compression distortion, while run-length encoding (RLE) further increases the compression without any loss of relevant signal information. The developed technique employs a modified thresholding. The wavelet filters based on beta function and its derivative, improves the compression of signal as compared to earlier existing thresholding technique. The simulation results clearly show the superiority of this technique in terms of compression ratio and a desirable signal quality. For performance evaluation, several significant parameters such as percent root mean square difference (PRD), signal-to-noise ratio (SNR) and compression ratio (CR) are used, whereas signal quality and its relevant information are evaluated by QRS peak detection.

Image Compression Based on Generalized Principal Components Analysis and Simulated Annealing

The authors propose a new data dimensionality reduction method that is formulated as an optimization problem solved in two stages. In the first stage, Generalized Principal Component Analysis (GPCA) is used to find a solution with local maximum (local solution) whereas the algorithm Simulated Annealing (SA) is performed, in the second stage, to converge the local solution to the optimal solution. The performance of GPCA and GPCA with Simulated Annealing (GPCA-SA) as images compressors was evaluated in terms of the Compression Peak Signal-to-Noise Rate (CPSNR), memory size necessary to store the resulting compressed image and Contrast-to-Noise ratio. The results show that GPCA and GPCA-SA requires the same amount of memory to store compressed data, but GPCA-SA provides better CPSNR than GPCA. They also compared the performance of our designed method with a wavelet-based compression technique widely used in medical imaging, known as Lifting, to demonstrate the efficiency of GPCA-SA in clinical application.

ECG Signal Compression using Optimum Wavelet Filter Bank Based on Kaiser Window

In this paper, an optimized wavelet filter bank based methodology is presented for compression of electrocardiogram (ECG) signal. The methodology employs new wavelet filter bank whose coefficients are derived with window techniques such as Kaiser Windows using simple linear optimization and run-length encoding (RLE). The Wavelet based compression techniques minimize the compression distortion, while RLE further increases the compression without any loss of relevant signal information. The developed technique employs a modified thresholding, which improves the compression of signal as compared to earlier existing thresholding technique. A comparative study of performance of different existing methods and the proposed wavelet filter is made in terms of compression ratio (CR), percent root mean square difference (PRD) and signal-to-noise ratio (SNR). When compared, the developed wavelet filter gives better compression ratio and also yields good fidelity parameters as compared to other methods. The simulation result included in this paper shows the clearly increased efficacy and performance in the field of biomedical signal processing.

New Virtual SPIHT Tree Structures for Very Low Memory Strip-Based Image Compression

Images obtained with wavelet-based compression techniques such as set-partitioning in hierarchical trees (SPIHT) yield very good results. However, a lot of memory space is required as the wavelet coefficients for the whole image need to be stored for the process of set-partitioning coding. In this letter, we propose new virtual SPIHT tree structures for very low memory strip-based image compression. The advantage of the proposed work is that it reduces the memory requirements for practical software and hardware implementations significantly without sacrificing performance.

Data Compression in Adaptive and 4D Radiotherapy

Adaptive and 4D radiotherapy manipulates information from multiple fractions/phases. Such information includes 4DCT and daily CT, daily doses and deformation maps. Typically, Adaptive/4D radiotherapy involved hundreds time of data than conventional radiotherapy. Such huge data pose significant disk/database storage and network burden. An efficient and effective data compression method is essential to make the adaptive/4D radiotherapy clinically applicable. We developed a wavelet-based compression technique to handle the huge data from adaptive/4D radiotherapy. We use this technique to compress various dose images and deformation maps. The dose images are tomotherapy daily doses of the prostate, head & neck and lung patients. The deformation maps are calculated between the different phase of 4DCT image or between the daily image and the planning CT image using an intensity based deformable registration technique. For a dimension of 256 × 256 × 100, the dose image is about 30MB, while deformation maps are three times larger (90 MB). To evaluate the information lost through the lossy compression, we compare the compressed image with original image. The difference is the compression error. Two metrics are used, standard error and maximum error. Figure 1 plots the errors vs. the compression ratio for a typical dose image and a deformation map. For dose image, the errors are represented as the percentage of the maximum dose (Dmax). At 100:1 compression, the standard error is ∼0.05% Dmax, the maximum error is ∼0.5% Dmax. For deformation map, the errors are represented in the unit of voxels. At 100:1 compression, the standard error is ∼0.01 voxels, and the maximum error is ∼0.18 voxels. Both dose and deformation map can be easily compressed with 100:1 ratio without any clinically significant effect. Under this compression level, as for the dose image, the maximum error at ∼1% Dmax and the standard deviation of errors at ∼0.1% Dmax is well within the uncertainty of dose calculation and dose measurement. As for the deformation map, the maximum error at ∼0.2 voxel and standard deviation of errors at ∼0.01 voxel is well-acceptable since the deformation maps are calculated from voxel-based CT image and only have voxel precision. With 100:1 compression ratio, for an adaptive therapy of 40 fractions, all 40 deformation maps and daily doses would only take extra storage that is less than 3–4 times of the planning CT image, instead of 300–400 times if not compressed. In conclusion, the wavelet compression can achieve great compression ratio while maintain the image quality relevant to clinical applications. It will make those advanced treatment techniques like adaptive or 4D to be practical.

Using standard image compression algorithms to store data from computational fluid dynamics

Three-dimensional numerical modeling of fluid flows is an important research tool to understand many fluid dynamical effects observed in nature. With the strong growth of available computational resources the use of such models has greatly increased over the last years. Because the available mass storage has not increased in the same order as the CPU speed many researchers nowadays face the problem of how to store and transfer the large data sets produced by the model calculations for post-processing. The use of lossy wavelet-based compression techniques on this data has been investigated in several publications. These techniques are often specialized to one problem and are not easy to implement. In the area of digital media, however, advances have been made for still image (JPEG, JPEG-2000) and motion image (MPEG) compression. In this paper we investigate the usefulness of these image compression algorithms for the storage of data from computational fluid dynamics on regular cartesian grids. We analyze both the compression ratios achieved and the error introduced by these lossy compression schemes. We found that, for our purposes, the JPEG compression scheme allows an easy-to-use, portable, robust, and computationally efficient lossy compression. For the easy use of these compression algorithms we present a simple wrapper library.

Applying wavelet-transform on Internet-based radiologic brain images

Wavelet-transform compression creates high-quality images of small file size by tuning its compression ratio between 10:1 and 80:1. We applied the wavelet-based compression technique to radiologic brain images on the Internet for image display purposes. Wavelet transform compression reduces file size up to 50%. Wavelet transform compression offers the advantage of retaining complete color information in digital images and faster Internet transmission than traditional Joint Photography Experts Group (JPEG) files of equivalent file size. The application of wavelet-based compression technique decreases storage requirements for images on Internet servers.

Adaptive image compression based on segmentation and block classification

This paper presents a new digital image compression scheme which exploits one of the human visual system properties-namely that of, recognizing images by their regions-to achieve high compression ratios. It also assigns a variable bit-count to each image region that is proportional to the amount of information the region conveys to the viewer. The new scheme copes with image non-stationarity by adaptively segmenting the image into variable-block sized regions, and classifying them into statistically and perceptually different classes. Then, blocks in each class are separately encoded. Based on extensive testing, the performance of the new scheme surpasses the performance of the JPEG standard and goes beyond its compression limits. In most test cases, the new compression scheme results in a maximum compression ratio that is at least twice of JPEG, while exhibiting lower objective and subjective image degradations. Moreover, the performance of the new block-based compression is comparable to the performance of the state-of-the-art wavelet-based compression technique and provides a good alternative when adaptability to image content is of interest.

Wavelet-based vector quantization for high-fidelity compression and fast transmission of medical images.

Compression of medical images has always been viewed with skepticism, since the loss of information involved is thought to affect diagnostic information. However, recent research indicates that some wavelet-based compression techniques may not effectively reduce the image quality, even when subjected to compression ratios up to 30:1. The performance of a recently designed wavelet-based adaptive vector quantization is compared with a well-known wavelet-based scalar quantization technique to demonstrate the superiority of the former technique at compression ratios higher than 30:1. The use of higher compression with high fidelity of the reconstructed images allows fast transmission of images over the Internet for prompt inspection by radiologists at remote locations in an emergency situation, while higher quality images follow in a progressive manner if desired. Such fast and progressive transmission can also be used for downloading large data sets such as the Visible Human at a quality desired by the users for research or education. This new adaptive vector quantization uses a neural networks-based clustering technique for efficient quantization of the wavelet-decomposed subimages, yielding minimal distortion in the reconstructed images undergoing high compression. Results of compression up to 100:1 are shown for 24-bit color and 8-bit monochrome medical images.

Plotting and Scheming with Wavelets

Wavelets are acquiring a visibility and popularity that may soon be on the scale first enjoyed by fractals a few years back. Like fractals, wavelets have attractive and novel features, both as mathematical entities and in numerous applications. They are often touted as worthwhile alternatives to classical Fourier analysis, which works best when applied to periodic data: wavelet methods make no such assumptions. However, the mathematics of wavelets can seem intractable to the novice. Indeed, most introductions to wavelets assume that the reader is already well versed in Fourier techniques. Our main goal is simple: to convince the reader that at their most basic level, wavelets are fun, easy, and ideal for livening up dull conversations. We demonstrate how elementary linear algebra makes accessible this exciting and relatively new area at the border of pure and applied mathematics. In Plotting, we explore several ways of visually representing data, with the help of Matlab software. In Scheming, we discuss a simple wavelet-based compression technique, whose generalizations are being used today in signal and image processing, as well as in computer graphics and animation. The basic technique uses only addition, subtraction, and division by two! Only later, in Wavelets, do we come clean and reveal what wavelets are, while unveiling the multiresolution setting implicit in any such analysis. In Averaging and Differencing with Matrices, which may be read independently of Wavelets, we provide a matrix formulation of the compression scheme. In Wavelets on the World Wide Web we mention a natural form of progressive image transmission that lends itself to use by the emerging generation of web browsers (such wavelet-enhanced software is already on the market). In Wavelet Details, we attempt to put everything in context, while hinting at the more sophisticated mathematics that must be mastered if one wishes to delve deeper into the subject. Finally, in Closing Remarks, we mention some other common applications of wavelets. Along the way we find ourselves trying out an adaptive plotting technique for ordinary functions of one variable that differs from those currently employed by many of today's popular computer algebra packages. While this technique, as described here, is limited in its usefulness, it can be modified to produce acceptable results. We were much inspired by Stollnitz, DeRose, and Salesin's fine wavelets primers ([15, 16]), which, along with [17], [18], [19], we recommend heartily to beginners wlho