Image Compression with Neural Networks Using Complexity Level of Images

This paper presents a complexity-based image compression method using neural networks. In this method, different multi-layer perceptron ANNs are used as compressor and de-compressor. Each image is divided into blocks, complexity of each block is computed using complexity measure methods and one network is selected for each block according to its complexity value. Three complexity measure methods, called entropy, activity and pattern-based are used to determine the level of complexity in image blocks and their ability are evaluated and compared together. Selection of a network for each image block is based on its complexity value or the Best-SNR criterion. Best-SNR chooses one of the trained networks such that it results best SNR in compressing a block of input image. In our evaluations, best results, with PSNR criterion, are obtained when overlapping of blocks is allowed and choosing the networks in compressor is based on the Best-SNR criterion. In this case, the results demonstrate superiority of our method comparing with previous similar works and that of JPEG standard coding.