文档介绍：Abstract
Compressed Sensing theory is a new codec method which was proposed in recent years. It breaks the Nyquist sampling theorem constraints. Compared to the traditional codec theory, the greatest advantage pressed sensing codec theory is its encoding process is extremely simple, plexity is transferred to the decoding side, which has its unique advantages in strong mobility, puting power and lower storage capacity occasion. pared to the traditional codec theory, signal reconstruction quality pressed sensing codec method remains to be improved. The main purpose of this paper is to optimize the coding method pressed sensing, including optimization of the measurement matrix, adaptive selection of measurements, measurements quantization method.
As the coding method pressed sensing is a projection process of the signal onto a measurement matrix, the quality of measurement matrix directly affects the codec quality, so the paper starts from optimizing of the measurement matrix, Two measurement matrix optimization algorithms is proposed: one is the upper triangular weighted measurement matrix optimization algorithm, which can enhance the sampling of low frequency coefficients; the other is the gradient-based Gram matrix iterative optimization, which can reduce the relevance of measurement matrix and sparse matrix. Experiments show that these two optimizations can improve the performance of existing measurement matrix.
Secondly, a maximum posteriori variance based adaptive selection of measurements algorithm is proposed, which is mainly about "For a known measurement matrix, selecting which lines can get the best measurements" and "Selecting how many lines are adequate for the current signal". According to this algorithm, a measurement matrix which has large number rows is selected first, then selecting the right rows according to this algorithm. Experimental results show that the proposed adaptive algorithm can get better reconstruction pared to the conventional measu