Lossless Data Compression with Neural Network Based on Maximum Entropy Theory

Neural networks are used more frequently in lossy data coding domains such as audio, image, etc than in general lossless data coding, because standard neural networks must be trained off-line and they are too slow to be practical. In this paper, an adaptive arithmetic coding algorithm based on maximum entropy and neural networks are proposed for data compression. This adaptive algorithm with simply structure can do on-line learning and does not need to be trained off-line. The experiments show that this algorithm surpasses those traditional coding method, such as Limper-Ziv compressors (zip, gzip), in compressing rate and is competitive in speed and time with those traditional coding method such as PPM and Burrows-Wheeler algorithms. The compressor is a bit-level predictive arithmetic which using a 2 layer network with muti-input and one output. The arithmetic, according with the context constriction, improves the precision of prediction and reduces the coding time.