Research On Joint Source-Channel Coding System Based On Image Compressed Sensing

According to the Shannon separation principle, the traditional communication system designs the source coding and channel coding separately. The goal of the source encoder is to obtain the maximum compression ratio without considering the specific characteristics of the channel, while the channel encoder is to obtain the minimum error probability without protecting the source information pertinently based on its distribution characteristics. However, most of the communication systems, such as cellular communications, broadcast communications, underwater acoustic communications with severe multipath, etc, can not meet the prerequisites to achieve optimal system performance of the Shannon separation principle. Therefore, it is necessary to consider the source and channel factors comprehensively in the practical application so that the channel resources can be allocated optimally and the system can achieve the optimal performance from end to end. This paper constructs a Joint Source-Channel Coding system based on the image compressed sensing, which combines the double unequal error protection and dynamic allocation mechanism. The main research work and innovations are as follows:1. Study the principle, classification and classic algorithms of the source coding and channel coding, and achieve a number of the classical coding algorithm through simulation. On this basis, the principle, classification, advantages and scope of the Joint Source-Channel Coding theory is emphatically studied.2. Study the compressed sensing theory deeply, and apply it into a Joint Source-Channel Coding system. This method not only demonstrates advantages of high compression radio and accurate reconstruction performance of the compressed sensing compared with the conventional compression encoding method, but also reduces the amount of the data greatly under the same reconstruction accuracy.3. Propose a design method of an observation matrix based on the tent chaos. The observation matrix overcomes the shortcomings such as the uncertainty, hardware implementation and storage of the typical random measurement matrix by utilizing the excellent pseudo-random of the chaotic sequence. Analyze the construction performance of the observation matrix with three typical random observation matrix comparatively. The simulation result shows that the chaos tent observation matrix obtains better reconstruction performance under the same compression ratio.4. Propose a Joint Source-Channel Coding method based on the image compressed sensing. The method combines the double unequal error protection and dynamically rate allocation together, encoding according to the distribution characteristics of the source. In the source coding section, use two-dimensional discrete wavelet transform to separate the low frequency components which is important and the less important component. Then use compressed sensing compress the component unequally based on the importance of each component. In the channel coding section, adjust the encoding rate of Turbo codes dynamically according to the progressive of code stream so to achieve the unequal error protection channel. The simulation experiments shows that for image signals with different types and amounts of data, the system can still obtain high reconstruction accuracy with the compression ratio of 7: 1. And the system can also reduce the energy consumption and increase the transmission efficiency.5. Establish a double compression method which combines the lossy coding and lossless coding together. In the source coding module, combines the compressed sensing with Huffman entropy coding. Huffman entropy coding can obtain the compression ratio from 2: 1 to 5: 1 as an optimal lossless compression method. Therefore, the system can achieve higher compression ratio with the combination of the compressed sensing and Huffman entropy coding.