Image Compression Coding Method Based On Fractal Theory And Wavelet Transform

The application of fractal theory to image compression coding has been attracting the attention of scholars all over the world.Because of its novel thought,high compression ratio and fast decoding,we are constantly exploring to update its applicability.However,it takes a lot of time to search the best matching block in the coding process,which reduces the rate of the fractal image coding.Therefore,improving the quality of reconstructed images and speeding up the encoding and decoding of images are the topics discussed in this paper.The specific work is as follows:Firstly,an improved algorithm of reducing the search space is proposed by using the self-similarity of the image sub-block itself and its relationship with the root mean square error of matching,which can shorten the encoding time of the image and further improve the quality of the reconstructed image.Simulation results show that the proposed algorithm not only shortens the time of image encoding and decoding,but also improves the quality of the reconstructed image.Secondly,the wavelet transform is used to process the image first,then the fractal feature is introduced to establish the inequality between the sub-block and the root mean square error.After simulation experiments,this algorithm not only shortens the time of image encoding and decoding,but also improves the quality of the reconstructed image.Finally,the parent block library is divided into three categories in advance,and the corresponding features are used in each class to limit the search range to a neighborhood close to the eigenvalue of the sub-block,which effectively reduces the search object and is further effective.The simulation results show that compared with other algorithms,the time spent on image coding is significantly shortened and the encoding speed of the algorithm is significantly improved.