Research On Fractal Theory In Image Compression And Image Retrieval

Fractal image compression coding algorithm is a novel image compression technology and has high compression ratio.However,the long encoding time and unacceptable image reconstruction quality remain the primary obstacles in practical application.In order to balance the relationship between coding time,image reconstruction quality and compression ratio,this paper focuses on improving the image reconstruction quality and coding speed.In view of these problems of fractal coding algorithm,the main research work is as follows:(1)For some images with the low structural similarity,a novel orthogonal sparse fractal coding algorithm based on image texture feature is proposed.First,orthogonal sparse grey level transform based on sparse decomposition and orthogonalization fractal coding algorithm improves image reconstruction quality.Then,the similarity measure matrix,which stores the variance feature between range blocks and domain blocks,is used to reduce redundancies and encoding time.Simulation results show that the proposed algorithm in this study can obtain better image reconstruction quality and speed up encoding time significantly as compared to the baseline fractal image compression and other algorithms based on sparse decomposition.(2)In order to solve the conflicting problem,fast sparse fractal coding algorithm based on wavelet transform is proposed.Combined with the strong sparsity of wavelet coefficients,sparse decomposition is used to propose sparse gray level transform based on wavelet transform.Then,by extracting the skewness and kurtosis of the image as feature vectors to calculate similarity,the redundancy in the virtual codebook is reduced and the encoding time is shortened.The simulation results show that the proposed hybrid fractal coding algorithm based on the previous chapter gets more efficient coding quality and achieves the expected results.(3)In view of the fact that fractal image compression coding is easy to lose the internal image structure information during segmentation,a fractal image compression coding algorithm based on tensor decomposition and dictionary learning is proposed.Firstly,the range block pool and virtual codebook are regarded as the high-order tensor.Then,the virtual codebook with block sparse characteristics is trained according to the texture features of the image.Finally,the Tucker decomposition of tensor and fractal coding are combined to carry out the gray level matching process.Experimental results show that the performance of the new algorithm is better and achieves the expected results.