Source Coding for Text Transmission using a Deep Neural Network as a Lossy Compression Stage

The continuous growth of traffic in the telecommunication networks has motivated the search for optimal source codes that can achieve high percentages of compression of the information to be transmitted. However, the compression rates are limited in the practice for the type of messages to encode. For this reason, new techniques have been developed in order to improve the compression rates of the traditional algorithms. In particular, source coding techniques based on computational intelligence algorithms are being studied lately. Hence, this paper proposes a new source coding technique for text compression based on two stages: the initial stage uses a deep neural network, called Text Embedding Neural Network, and the second stage uses a Canonical Huffman Code. The deep neural network increases the compression rate by controlling the level of syntax loss allowed in each message through a single adjustable parameter. This combination is able to reduce the size of the transmitted messages up to 30% with relation to only use traditional source coding algorithms.