Adaptive Neuro-Fuzzy Inference System based Fractal Image Compression

This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for fractal image compression. One of the image compression techniques in the spatial domain is Fractal Image Compression (FIC) but the main drawback of FIC using traditional exhaustive search is that it involves more computational time due to global search. In order to improve the computational time and compression ratio, artificial intelligence technique like ANFIS has been used. Feature extraction reduces the dimensionality of the problem and enables the ANFIS network to be trained on an image separate from the test image thus reducing the computational time. Lowering the dimensionality of the problem reduces the computations required during the search. The main advantage of ANFIS network is that it can adapt itself from the training data and produce a fuzzy inference system. The network adapts itself according to the distribution of feature space observed during training. Computer simulations reveal that the network has been properly trained and the fuzzy system thus evolved, classifies the domains correctly with minimum deviation which helps in encoding the image using FIC.