A Novel Hybrid Medical Data Compression Using Huffman Coding and LZW in IoT

Abstract

Today, telemedicine has become very essential, because it provides the possibility for the health centers, hospitals, and research centers to exchange medical and diagnostic data, via IoT. Since the volume of the generated medical information in IoT is very high, transmitting this data via channels of limited bandwidth is time-consuming; thus, the information should be compressed before transmission. Todays, some of the methods compress data significantly, but quality of the restored data in these methods is very low. Considering the importance of images and information for diagnostic and medical applications, desired quality is of great importance. Thus, this study tries to present a hybrid medical information compression technique such that quality of the restored information is desired and compression is efficient. This compression technique combines two lossless compression methods, including Huffman encoding and Lempel-Ziv-Welch (LZW) that rearranges information. In this combination, a binary information arrangement is used between Hoffman and LZW techniques so that the integration of binary information reaches an information mapping and, while simplifying the mapping, is completely different for each piece of information and makes this article stand out. The simulation results of the proposed method show that medical information including signal, text, and image is reduced by an average of 37.85%. Remarkably, quality of the restored image is not degraded.