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Hybrid Image Compression Encoding Based on IFS Theory
Author: LiFanPing
Tutor: WangXingYuan
School: Dalian University of Technology
Course: Applied Computer Technology
Keywords: Fractal Image Compression Iterated function system Affine transformation Spatial correlation
CLC: TP391.41
Type: Master's thesis
Year: 2007
Downloads: 62
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Abstract
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Fractal image compression method developed in the past ten years, a new image compression algorithm, the idea comes mainly from fractal images can be used iterated function systems (Iterated Function System, IFS) to generate. Real-life images are the existence of a self-similar, just be able to find a set of coding image affine transformation, the original image with affine transform to represent a fixed point, we realized the original image compression. Fractal image compression file is stored in the affine transformation parameters quantized value instead of the pixel values ??of the image itself, in order to achieve the high magnification image data compression. In this paper, fractal image compression algorithm is relatively low, the encoding time is too long and other issues, made the following aspects of work: First, the use of natural gray value of pixels in the image spatial correlation of evolutionary algorithms and global search ability, the predecessors Based on the study, to achieve a spatial correlation of image-based hybrid genetic algorithm and simulated annealing fractal image compression. The algorithm will limit the range block search space of eight blocks of the neighborhood and its extension, reducing the range block the search space; and when the similarity error can not meet the application requirements, the use of hybrid genetic algorithm to find the optimal global scope solution, to ensure the quality of the reconstructed image. Experimental results show that the reconstructed image compression method SNR decreased slightly, but both encoding time and the compression ratio has been improved. Secondly, based on the traditional variance fractal compression algorithm, based on research of a hybrid neural network based on variance and fast fractal encoding methods. The method according to the statistical properties of the image block, the variance is less than a given threshold value using the affine transform coding range blocks, the range block is greater than the threshold value is encoded using a hybrid neural networks. Improved methods not only solve the large changes in the gray block matching error big problem, and overcome the BP algorithm, slow convergence and local convergence defects. Experimental results demonstrate the effectiveness of the method. Finally, no search fractal image coding based on the study, improved neighborhood search algorithm, which is based on progressive / Split search strategy fractal image compression method. Firstly, the range blocks 5 neighborhood search, and in the direction of the optimal solution can be extended when the results did not improve when the match, it will range Kuaikuai quadtree segmentation, which is divided into size, shape, the same four sub-blocks, proceed to match your search. Experimental results demonstrate the effectiveness of the method.
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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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