Medical Data Compression for Lossless Data Transmission and Archival

Abstract

Medical applications create an enormous amount of data. Medical data transmission via networks necessitates a huge bandwidth rate. Also, digital medical data necessitate enormous storage and archive. With the evolution of the Internet and multimedia designs, medical data is required to be transmitted in a rapid manner. One of the practical solutions to this issue is medical data compression. Data compression (DC) and transmission is important in the medical field. DC is used to transmit a large amount of data for minimizing the cost. DC is introduced to minimize the image for focusing on the removal of redundant data. DC is classified into two categories, namely, lossy and lossless techniques. DC is designed to reduce storage, bandwidth, and time consumed for transmission. Coding is utilized to remove unwanted data. The different DC algorithm is used to enhance the compression rate. Some of the medical data compression techniques are outlined to lessen data redundancy via specialized data coding and, as a result, can significantly minimize the constructive amount of medical data. In other words, medical data compression involves the procedure of encoding medical data in such a manner that less storage is essential to archive them over a network. The contemporary prototype of medical data compression is split into two stages, namely, designing and entropy coding. Selecting the appropriate prototype is paramount due to the reason that the more consistencies we identify, the more are the probabilities to minimize the series scope. Next, based on the understanding acquired via designing, unwanted data are eliminated by applying coding. Here, encoding is performed to eliminate dispensable data. As several DC techniques have been progressed, a requirement comes to light to assess the techniques, and an endeavor is made to review and classify different DC techniques based on three classifications, namely, coding schemes, data quality specifications, and application appropriateness. Some of the coding schemes for lossless data compression, to name a few, are run-length encoding, Huffman encoding, and LZW encoding. Also with the expeditious rise in high-speed data acquisition, bandwidth acquisition and storage have become the focal restrictions concerning DC techniques. We observed that it is impracticable to outline an exclusive lossless compression technique for different data types without a certain understanding of the series. It is also unfeasible to develop a disparate lossless compression algorithm for every potential series. The intelligent alternative is to devise comprehensive DC and to utilize such an algorithm to the series that can be handled, with a higher amount of precision. Some of the analyzed algorithms are component analysis, partial matching, state-space transitions, and tree sequence.