Coding Algorithm For Image Compression Based On Sparse Decomposition

Sparse decomposition becomes very popular in the study of signal processing in the recent years, because it can transform one signal into a sparse formation. Sparse decomposition is therefore applied to image processing quickly. Image compression is one of key steps in image processing and it has been widely applied to many fields of modern sciences and technologies. A lot of image compression methods have been proposed after many years study. And these methods, generally based on orthogonal transform. However, for image compression at low bit rate, these methods usually don't perform as good as at high bit. So it is necessary to develop a new method for image compression at low bit rate.In this thesis, the image compression is studied based on sparse decomposition and a new image coding scheme is proposed based on the result of image sparse decomposition.In this thesis, the image sparse decomposition is introduced firstly, and the characteristic and key problems of image sparse decomposition are mentioned. As the formation of image sparse decomposition result, sparse image presentation is introduced.In the following, matching pursuit, which is the most popular algorithm of image sparse decomposition, is introduced. Matching pursuit is much easier to understand and to apply than other algorithms, but it also has great computational burden. Therefore genetic algorithm is applied to MP-based image sparse decomposition. But the original genetic algorithm can't still afford the image decomposition, so, in order to have good reconstructed image quality and low computational burden, the genetic algorithm used in this thesis, is improved through many other methods.At the end, the experimental results show that, at low bit rate, the newly presented method outperforms many other image compression methods in the objective and subjective qualities of decoded images.