As Hadamard measurement matrix cannot be used for compressing signals with dimension of a non-integral power-of-2, this paper proposes a construction method of block Hadamard measurement matrix with arbitrary dimension. According to the dimension N of signals to be measured, firstly, construct a set of Hadamard sub matrixes with different dimensions and make the sum of these dimensions equals to N. Then, arrange the Hadamard sub matrixes in a certain order to form a block diagonal matrix. Finally, take the former M rows of the block diagonal matrix as the measurement matrix. The proposed measurement matrix which retains the orthogonality of Hadamard matrix and sparsity of block diagonal matrix has highly sparse structure, simple hardware implements and general applicability. Simulation results show that the performance of our measurement matrix is better than Gaussian matrix, Logistic chaotic matrix, and Toeplitz matrix.
In order to improve the accuracy of global motion estimation (GME), a new method for GME combining with motion segmentation is proposed in this paper. The proposed method removes motion vector (MV) outliers and implements initial motion segmentation by analyzing properties of motion vectors. Using the filtered MV field, global motion parameters were estimated, and then the difference frame was generated by global motion compensation（GMC ）. According to the movement difference between the background and the foreground regions and movement consistency in the same region, the absolute sum of difference frame in every block was calculated, and thus adaptively generating the threshold value to detect motion regions. MVs in the motion regions were rejected as outliers for GME, and iterative computations between GME and motion segmentation were performed successively. Experimental results demonstrate that the proposed approach can effectively extract motion regions, thus enhancing the accuracy of GME.