Regarding the problems of insufficient image segmentation intelligence, low compression rate, slow speed for global searching to find the optimal fractal image compression encoding, and bad decoding effect, this article proposes the fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming. Firstly, according to the accurate and fast image classification of deep convolutional neural network and the fast search and matching encoding advantages of gene expression programming, it realizes theoretically the action mechanism of fractal image compression hybrid encoding by combining the convolutional neural network and the gene expression programming; then, it uses the deep convolutional neural network to train and classify the image, and uses the adaptive quadtree segmentation method to segment the classified image, thus generating the domain block and range block classification set. According to the action mechanism of gene expression programming in fractal image compression encoding, it then quickly obtains the optimal solution of fractal image compression encoding by searching and encoding the sub-blocks of range block classification set and the classification set corresponding to the domain. Finally, in the CPU/GPU environment, it conducts the comparative experiment with basic fractal image compression algorithm and fractal image compression algorithm based on convolutional neural network. The experimental results show that this proposed algorithm outperforms similar algorithms in terms of image segmentation speed and accuracy as well as fractal compression encoding speed and compression ratio. Therefore, this algorithm is a fractal image compression algorithm with intelligent segmentation, fast encoding and high compression ratio.
CAS-CNN: A deep convolutional neural network for image compression artifact suppression
