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Image Compression Based on Integer Wavelet Transform and Improved Embedded Zerotree Wavelet Encoding

Ding Xu-xing,Zhu Ri-hong,Li Jian-xin,

电子与信息学报 , 2004,
Abstract: The main advantages of Integer Wavelet Transform(IWT) are that the input and output values are all integers, all operations can be done in place, only a small memory is required and easy to be implemented in hardware. In the context of image coding, IWT is well suited for lossless compression. However, it performs a little worse compared to the conventional Discrete Wavelet Transform(DWT) for lossy compression. In this paper, a new algorithm is proposed for improving lossy compression performance of IWT. It is by means of the IWT based on lifting scheme combining with improved Embedded Zerotree Wavelet(EZW) based on morphological dilation operation. Simulation results show the proposed algorithm improves the Peak Signal Noise Ratio(PSNR) compared to conventional DWT without increasing computational complexity.
Integer Wavelet Transform and Predictive Coding Technique for Lossless Medical Image Compression  [PDF]
Vineeta Gupta,A.G. Rao,Krishna Mohan Pandey
International Journal of Technological Exploration and Learning , 2013,
Abstract: Lossless image compression has one of its important application in the field of medical images. Enormous amount of data is created by the information present in the medical images either in multidimensional or multiresolution form.Efficient storage, transmission, management and retrieval of the voluminous data produced by the medical images has nowadays become increasingly complex.Solution to the complex problem lies in the lossless compression of the medical data .Medical data is compressed in such a way so that the diagnostics capabilities are not compromised or no medical information is lost.This paper proposes a hybrid technique for lossless medical image compression that combines integer wavelet transforms and predictive coding to enhance the performance of lossless compression. Here we will first apply the integer wavelet transform and then predictive coding to each subband of the image obtained as an output to lifting scheme.Measures such as entropy,scaled entropy and compression ratio are used to evaluate the performance of the proposed technique.
A New Approach Based on Shapiro`s Embedded Zerotree Wavelet (Ezw) Algorithm for Image Compression
A. Ouafi,Z.E. Baarir,A. Taleb Ahmed,N. Doghmane
Asian Journal of Information Technology , 2012,
Abstract: In this paper, we propose a new study to image compression based on the principle of Shapiro`s Embedded Zerotree Wavelet (EZW) algorithm. Our study, the modified EZW (MEZW), distributes entropy differently than Shapiro`s and also optimizes the coding. This study can produce results that are a significant improvement on the PSNR and compression ratio obtained by Shapiro, without affecting the computing time. These results are also comparable with those obtained using the SPIHT and SPECK algorithms.
An Image Wavelet Compression Algorithm Based on Zerotree and Bit Plane

NIU Jian-weix,WANG Ren,LI Bo,

软件学报 , 2002,
Abstract: By combining Zerotree, bit plane and arithmetic coding together, a new image compression algorithm based on Zerotree and bit plane called ZBP is presented in this paper. ZBP exploits the correlation among the Zerotree symbols and the bit data of wavelet coefficients, so the efficiency of arithmetic coding is improved. Experimental results demonstrate that ZBP performs better compression than the existing wavelet imagecompression algorithms.
An Optimum Approach for Image Compression: Tuned Degree-K Zerotree Wavelet Coding
Li Wern Chew,Wai Chong Chia,Li-minn Ang,Kah Phooi Seng
IAENG International Journal of Computer Science , 2009,
An RGB Image Encryption Supported by Wavelet-based Lossless Compression
Ch. Samson,V. U. K. Sastry
International Journal of Advanced Computer Sciences and Applications , 2012,
Abstract: In this paper we have proposed a method for an RGB image encryption supported by lifting scheme based lossless compression. Firstly we have compressed the input color image using a 2-D integer wavelet transform. Then we have applied lossless predictive coding to achieve additional compression. The compressed image is encrypted by using Secure Advanced Hill Cipher (SAHC) involving a pair of involutory matrices, a function called Mix() and an operation called XOR. Decryption followed by reconstruction shows that there is no difference between the output image and the input image. The proposed method can be used for efficient and secure transmission of image data.
A Wavelet Image Compression Algorithm Based on Fractal Coding and Zerotree

ZHANG Hong-ying,YANG Chang-sheng,

中国图象图形学报 , 2003,
Abstract: In order to achieve a high image compression ratio in fractal cloding, the ability of fractal coding to predict wavelet coefficients is anyalyzed and the traditional way of fractal coding is found to be not able to effectively predict the entire wavelet coefficients and leads to a not very good coding result. A hybrid image compression algorithm based on wavelet transforming using fractal coding and zerotree coding that can make up for this flaw effectively is presented in this paper. First, the image is discomposed into a series of subimages in different orientations and different resolutions by wavelet transform, then the subimages in the same orientations but different resolutions are formed into wavelet subtrees, just like zerotree,at last ,the wavelet subtrees are coded by the way of either fractal or zerotree coding according to the size of error when coding.. This algorithm made a effective use of the redundance within subimages as well as the self-similarities within subimages and the similarities cross scales compared with traditional fractal image coding based on wavelet transforming. The experimental with this algorithm presented in this paper also show that the proposed algorithm can obtain a good compression result in a broad compression rate scale.
Lossless Medical Image Compression by Integer Wavelet and Predictive Coding  [PDF]
T. G. Shirsat,V. K. Bairagi
ISRN Biomedical Engineering , 2013, DOI: 10.1155/2013/832527
Abstract: The future of healthcare delivery systems and telemedical applications will undergo a radical change due to the developments in wearable technologies, medical sensors, mobile computing, and communication techniques. When dealing with applications of collecting, sorting and transferring medical data from distant locations for performing remote medical collaborations and diagnosis we required to considered many parameters for telemedical application. E-health was born with the integration of networks and telecommunications. In recent years, healthcare systems rely on images acquired in two-dimensional domains in the case of still images or three-dimensional domains for volumetric video sequences and images. Images are acquired by many modalities including X-ray, magnetic resonance imaging, ultrasound, positron emission tomography, and computed axial tomography (Sapkal and Bairagi, 2011). Medical information is either in multidimensional or multiresolution form, which creates enormous amount of data. Retrieval, efficient storage, management, and transmission of these voluminous data are highly complex. One of the solutions to reduce this complex problem is to compress the medical data without any loss (i.e., lossless). Since the diagnostics capabilities are not compromised, this technique combines integer transforms and predictive coding to enhance the performance of lossless compression. The proposed techniques can be evaluated for performance using compression quality measures. 1. Introduction Applications involve image transmission within and among health care organizations using public networks. In addition to compressing the data, this requires handling of security issues when dealing with sensitive medical information system. Compressing medical data includes high compression ratio and the ability to decode the compressed data at various resolutions. In order to provide a reliable and efficient means for storing and managing medical data computer-based archiving systems such as Picture Archiving and Communication Systems (PACSs) and Digital Imaging and Communications in Medicine (DICOM), standards were developed with the explosion in the number of images acquired for diagnostic purposes; the importance of compression has become invaluable in developing standards for maintaining and protecting medical images and health records. Compression offers a means to reduce the cost of storage and to increase the speed of transmission. Thus, medical images have attained a lot of attention towards compression. These images are very large in size and require a
A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors  [PDF]
N. Sriraam
International Journal of Telemedicine and Applications , 2012, DOI: 10.1155/2012/302581
Abstract: Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications. 1. Introduction Medical signal processing is a fast growing field of research that is producing increasingly sophisticated applications in today’s high-tech medicine [1–8]. In the field of neurology, EEG, the manifestation of brain’s electrical activity as scalp potentials, remains as one of the commonly used noninvasive techniques for understanding brain functions in health and disease. Since its discovery by Berger [9], many research activities have centered on how to automatically extract useful information about the brain’s conditions based on the distinct characteristics of EEG signals. Many applications require acquisition, storage, and automatic processing of EEG during an extended period of time [4, 10–20]. For example, 24?h monitoring of a multiple-channel EEG is needed for epilepsy patients. The frequency range of a normal adult EEG lies between 0.1–100?Hz; thus, a minimum sampling rate of 200?Hz is needed. At the quantization level of 16 bit/sample, a 10-channel EEG for a 24?h period would require storage space of 346?Mb. Furthermore in order to diagnose the disease and to assess the effectiveness of the treatment via the brain functions, the
Visually Improved Image Compression by Combining EZW Encoding with Texture Modeling using Huffman Encoder  [PDF]
Vinay U. Kale,Shirish M. Deshmukh
International Journal of Computer Science Issues , 2010,
Abstract: This paper proposes a technique for image compression which uses the Wavelet-based Image/Texture Coding Hybrid (WITCH) scheme [1] in combination with Huffman encoder. It implements a hybrid coding approach, while nevertheless preserving the features of progressive and lossless coding. The hybrid scheme was designed to encode the structural image information by Embedded Zerotree Wavelet (EZW) encoding algorithm [2] and the stochastic texture in a model-based manner and this encoded data is then compressed using Huffman encoder. The scheme proposed here achieves superior subjective quality while increasing the compression ratio by more than a factor of three or even four. With this technique, it is possible to achieve compression ratios as high as 10 to 12 but with some minor distortions in the encoded image.
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