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Sparse decomposition - based image compression coding algorithm

Author: LinJin
Tutor: ShaoHuaiZong；LinWenZuo
School: University of Electronic Science and Technology
Course: Electronics and Communication Engineering
Keywords: Image processing Image Compression Sparse decomposition Sparse representation
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 427
Quote: 2
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Abstract


Sparse decomposition signal processing in recent years, a hot research field, it can signal a sparse form, causing the attention of researchers. Image compression is an important part of image processing, it is widely used in many areas of modern science and technology. After years of research, it has been proposed a variety of image compression method, and achieved good results in many areas. The existing image compression technique is generally orthogonal transform-based compression method. However, when in a high image compression ratio, i.e. a low bit rate image compression, the compressed image restoration effect is often not very good. Therefore need to develop a low bit rate case effective image compression method. Based on the good characteristics of sparse decomposition, one-dimensional signal sparse decomposition was soon extended to the image processing technology. This paper focuses on the sparse decomposition in the field of image compression based on image sparse decomposition results data compression coding scheme. This paper first analyzes the image sparse decomposition ideas, pointed out that the image sparse decomposition characteristics and problems need to be solved. Image Sparse Decomposition representation - the image sparse representation. Next, the article describes the image the sparse decomposition most commonly used algorithm - matching tracking algorithm. Compared with other sparse decomposition algorithm, the image matching tracking algorithm is easy to understand, easy to implement, but there's still the problem of the large amount of calculation. This article uses genetic algorithm to achieve image matching tracking sparse decomposition. Simple genetic algorithm still can not effectively reduce the amount of computation for image sparse decomposition According to the characteristics of the image sparse decomposition algorithm used in this article use a variety of optimization method of genetic algorithm improvements have been made in the calculation of the amount and the quality of the reconstructed image to obtain a better balance. Based on the image sparse decomposition analysis, we first studied the image sparse decomposition law of the distribution of the resulting data, and on this basis for result data to quantify coding scheme. This quantization coding scheme to achieve a sparse decomposition-based image compression encoding. In low bit-rate conditions, when the compression ratio is the same, the quality of the reconstructed image compression method in this article is superior to conventional image compression coding method reconstructed image quality. Finally, the test results show that the proposed coding scheme can effectively reduce the sparse decomposition result data projector component redundancy, and thereby improve the image sparse decomposition result data coding efficiency. The low bit-rate compression of the image at the same time, we use the method to restore the quality of the image is much better than the quality of the reconstructed image of a variety of existing image compression 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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