Information Theory and Coding: Techniques for Error Control and Data Compression

Abstract

This research focuses on the modern approaches to error control coding and data compression for solving the modern problems in digital communication and compare the performance of Hamming code, Reed-Solomon code, Turbo code, and Low Density Parity Check (LDPC) code in terms of Bit Error Rate (BER), Frame Error Rate (FER) and Decoding Time. This work proves that although Reed-Solomon and LDPC codes are effective in correcting errors in high noise level, they require more complex decoding procedures as compared to Hamming and Turbo codes. On the compression front, Huffman coding, Arithmetic coding, Context Based Adaptive Binary Arithmetic Coding (CABAC) and Deep learning methods are compared for their performance. What has been found in the outcome is that Arithmetic coding and CABAC have better compression ratios as compared to the Huffman coding, while the deep learning techniques are reported to give extraordinary performance all the more for the large data sets. This research illustrates that while there are numerous techniques available, each comes with a certain level of computational complexity and therefore, one has to make a decision depending on the specific need of the application. The findings are important for the further enhancement of error control and compression schemes in digital communication systems and for the development of new approaches and methods for their application.