A novel and comprehensive compressive sensing-based system for data compression

Data compression is one of challenging problems in data communication system due to the information explosion. In his paper, we propose a novel and comprehensive compressive sensing-based system for data compression. Performance of our proposed system is compared with conventional compression algorithm such as Huffman coding in terms of mean square error (MSE) after decompression, computation complexity and compression ratio, etc. As an application example, we implement his system to real world wind tunnel data. Simulation results show that our system can yield comparable or even beer compression as Huffman coding in terms of information loss. The major drawback of Huffman coding is to calculate the probability of each symbol which means it is not be appropriate for real time coding due to large amount of calculation. Meanwhile, our proposed system can process data by multiplying original data with Gaussian or Bernoulli sensing matrix directly which is also easy to implement.