Sparse Decomposition And Compression Of Digital Image Using Differential Evolution

With the improvement of information society, we can see the image information everywhere. The image compression, as an important part of image processing, is also widely used in modern science and technology fields. At present, people have made a variety of image compression methods, which formed a series of image compression standard. These standard are mostly based on image orthogonal transformation, which can achieve good results in the high bit rate compression, but the decoding image is not satisfactory under the conditions of low bit rate, and it can not meet people's demand for image compression. For example:for the JPEG compression standard, the following box will appear under the 0.2-bit, while for the JPEG2000 compression standard, mosquito-like noise will appear in conditions of low bit rate. Therefore it is necessary to develop an effective image compression method of low bit rate.In the recent years, sparse decomposition becomes very popular in the study of image processing. It is one kind of non-orthogonal decomposition, which decompose the image on the over-complete dictionary so as to get the image of the sparse representation. The decomposition result is very simple and consistent with human visual characteristics. It has become a new way of solving the image compression in low bit rate because that it can transform an image into a spare formation,.This paper mainly focuses on image compression based on the sparse decomposition. First to the large computation of image sparse decomposition, differential evolution algorithm is used; secondly this article has in-depth research to get the image of the sparse representation. On the basis of this, this paper researches some efficient coding methods. The main work and research results are as follows:1. The principle of image sparse decomposition and image sparse representation are introduced. Fast algorithm based on differential evolution algorithm is used for the large computation issue of image sparse decomposition, and the decomposition process and implementation process is given.2. In order to get the high-quality image sparse decomposition atoms and projection components, this paper has the further study for differential evolution, and presents the differential evolution algorithm based on the population diversity, which is used in image sparse decomposition. Firstly, the algorithm analyzes five different existing differential strategy and select the best one by comparing the reconstructed image quality of the sparse decomposition; Secondly considering of the issue that the diversity of population is down with the increase in the number of iterations in the optimization process, and measuring population diversity instead of a fixed number of iterations to find optimal conditions for the termination. The experimental results show that comparing with the particle swarm optimization algorithm of sparse decomposition and traditional differential evolution algorithm, this algorithm can effectively improve the peak signal noise ratio of reconstructed image under the same conditions, which can get the image representation more efficiently, quickly and accurately.3. According to the distribution of the six components of the image Sparse representation, a variable code length encoding compression algorithm is given after analysis of the traditional sort of lack of differential encoding. Firstly, the projection component of decomposition data is processed by the sort of projection differential, and then assigning a number of yards long by the component of atoms for each the differential component, two translational and one rotational component which have the larger amount of information, finally using fixed-length encoding for the two small-scale distribution components. The experimental results show that in the same conditions the algorithm can get a higher compression ratio compared with the algorithm of the literature under the same peak signal noise ratio of the compression reconstructed image, which can effectively reduce the coding redundancy and improve compression efficiency.4. To further improve the image compression ratio, the compression algorithm with Run-Length Encoding is given according to the parameters components characteristics of the image sparse representation, which use the idea of run-length encoding in the image compression based on the spare decomposition. The experimental results show that the algorithm can get a higher compression ratio at the same conditions by comparing with the varying code length algorithm and literature algorithm, which can improve the image compression efficiency.