Comparison of Wavelet Filters in Image Coding and Denoising using Embedded Zerotree Wavelet Algorithm

Abstract: In this study, we present Embedded Zerotree Wavelet (EZW) algorithm to compress the image using different wavelet filters such as Biorthogonal, Coiflets, Daubechies, Symlets and Reverse Biorthogonal and to remove noise by setting appropriate threshold value while decoding. Compression methods are important in telemedicine applications by reducing number of bits per pixel to adequately represent the image. Data storage requirements are reduced and transmission efficiency is improved because of compressing the image. The EZW algorithm is an effective and computationally efficient technique in image coding. Obtaining the best image quality for a given bit rate and accomplishing this task in an embedded fashion are the two problems addressed by the EZW algorithm. A technique to decompose the image using wavelets has gained a great deal of popularity in recent years. Apart from very good compression performance, EZW algorithm has the property that the bitstream can be truncated at any point and still be decoded with a good quality image. All the standard wavelet filters are used and the results are compared with different thresholds in the encoding section. Bit rate versus PSNR simulation results are obtained for the image 256x256 barbara with different wavelet filters. It shows that the computational overhead involved with Daubechies wavelet filters but are produced better results. Like even missing details i.e., higher frequency components are picked by them which are missed by other family of wavelet filters

An Improved Image Compression Algorithm Based on Daubechies- Wavelets with Arithmetic Coding

In this paper, we present image compression techniques to utilizing the visual redundancy and investigated. To effectively define and utilize image compression context for natural image is difficult problem. Inspired by recent research in the advancements of image compression techniques, we propose Daubechies-Wavelet with arithmetic coding towards the improvement over visual quality rather than spatial wise fidelity. Image compression using Daubechies-Wavelet with arithmetic coding is quite simple and good technique of compression to produce better compression results. In this image compression technique we first apply Daubechies-Wavelet transform then 2D Walsh-Wavelet transform on each kxk where (k=2n) block of the low frequency sub band. Split all values from each transformed block kxk followed by applying arithmetic coding for image compress. Index Terms-Image Compression, Daubechies-Wavelet, Arithmetic codin

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

Distortion-constraint compression of three-dimensional CLSM images using image pyramid and vector quantization

The confocal microscopy imaging techniques, which allow optical sectioning, have been successfully exploited in biomedical studies. Biomedical scientists can benefit from more realistic visualization and much more accurate diagnosis by processing and analysing on a three-dimensional image data. The lack of efficient image compression standards makes such large volumetric image data slow to transfer over limited bandwidth networks. It also imposes large storage space requirements and high cost in archiving and maintenance. Conventional two-dimensional image coders do not take into account inter-frame correlations in three-dimensional image data. The standard multi-frame coders, like video coders, although they have good performance in capturing motion information, are not efficiently designed for coding multiple frames representing a stack of optical planes of a real object. Therefore a real three-dimensional image compression approach should be investigated. Moreover the reconstructed image quality is a very important concern in compressing medical images, because it could be directly related to the diagnosis accuracy. Most of the state-of-the-arts methods are based on transform coding, for instance JPEG is based on discrete-cosine-transform CDCT) and JPEG2000 is based on discrete- wavelet-transform (DWT). However in DCT and DWT methods, the control of the reconstructed image quality is inconvenient, involving considerable costs in computation, since they are fundamentally rate-parameterized methods rather than distortion-parameterized methods. Therefore it is very desirable to develop a transform-based distortion-parameterized compression method, which is expected to have high coding performance and also able to conveniently and accurately control the final distortion according to the user specified quality requirement. This thesis describes our work in developing a distortion-constraint three-dimensional image compression approach, using vector quantization techniques combined with image pyramid structures. We are expecting our method to have: 1. High coding performance in compressing three-dimensional microscopic image data, compared to the state-of-the-art three-dimensional image coders and other standardized two-dimensional image coders and video coders. 2. Distortion-control capability, which is a very desirable feature in medical 2. Distortion-control capability, which is a very desirable feature in medical image compression applications, is superior to the rate-parameterized methods in achieving a user specified quality requirement. The result is a three-dimensional image compression method, which has outstanding compression performance, measured objectively, for volumetric microscopic images. The distortion-constraint feature, by which users can expect to achieve a target image quality rather than the compressed file size, offers more flexible control of the reconstructed image quality than its rate-constraint counterparts in medical image applications. Additionally, it effectively reduces the artifacts presented in other approaches at low bit rates and also attenuates noise in the pre-compressed images. Furthermore, its advantages in progressive transmission and fast decoding make it suitable for bandwidth limited tele-communications and web-based image browsing applications