A fuzzy and wavelet-based image compression algorithm : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand
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Date
2006
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Massey University
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Abstract
Nowadays, the Internet and digital image widespread are used in the industry, commerce, military, traffic and all walks of life. However, this kind of general use resulted in required for less transmission time and storage space. Image compression can address the problem of reducing the amount of data to represent a digital image. The image will satisfy the transmission and the preserved request after the compression. With the increasing use some technologies in the image processing, image compression also requires new technology to get the high compression ratio and more better image quality. Therefore, a new standard has been developed by Joint Photographic Experts Group (JPEG). Apart from JPEG, there are other algorithms developed for image compression, Normally, EZW, SHIPT and VQ algorithms. However, they all deal with the calculation of coefficients with too much complexity; as a consequence, compressing still image takes too much time. In the light of these problems, this thesis introduces a new method for dealing with the requirements of the coefficients while retaining the important detail in the image, by employing a Fuzzy Logic technique reduce the number of the coefficients, and then utilizes the Huffman or LZW algorithm to complete the image compression. The algorithm developed in this research, called IWF algorithm, is based on four key techniques: 1) a wavelet transform for decomposition. This technique allows the combination of lossless and lossy compression with extremely high compression rate and image quality. 2) Quantization, this technique generally works by compressing a range of value to a single quantum value. By reducing the number of discrete symbols in a given stream, the stream becomes more compressible. This step in the IWF process is a lossy transformation. 3) Adaptation of Fuzzy logic techniques. This step uses the Fuzzy Logic techniques to handle the wavelet coefficients, enable the wavelet coefficients to have the same value in the high subbands. 4) Adaptation of Lossless data compression techniques. Keywords: Image Compression, Fuzzy Logic, Wavelet transforms, Decomposition, Haar Wavelet transform.
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Image compression, Fuzzy logic