無損數據編碼領域應用較少。針對這種現狀，該文詳細地研究了最大熵統計模型和神經網絡算法各自的特點，提出了一種基于最大熵原理的神經網絡概率預測模型并結合自適應算術編碼來進行數據壓縮，具有精簡的網絡結構的自適應在線學習算法。試驗表明，該算法在壓縮率上可以優于目前流行的壓縮算法Limpel-Zip(zip,gzip)，并且在運行時間和所需空間性能上同PPM和Burrows Wheeler算法相比也是頗具競爭力的。該算法實現為多輸入和單輸出的兩層神經網絡，用已編碼比特的學習結果作為待編碼比特的工作參數，符合數據上下文相關約束的特點，提高了預測精度，并節約了編碼時間。
關 鍵 詞 算術編碼; 數據壓縮; 最大熵; 神經網絡
Lossless Data Compression with Neural Network Based on Maximum Entropy Theory
FU Yan，ZHOU Jun-lin，WU Yue
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.
Key words arithmetic encoding; data compression; maximum entropy; neural network