Focused on the high computational complexity and long encoding time problems in the traditional fractal image compression, an orthogonalized fractal encoding algorithm based on texture features of gray-level co-occurrence matrix was proposed. Firstly, from the perspective of feature extraction and image retrieval, the similarity measurement matrix between range blocks and domain blocks was established to transform the global search into the local search, so as to reduce the codebook. Then, by defining a new normalized block as the new gray-level description feature, the transformation process between blocks was simplified. Finally, the concept of Simultaneous Orthogonal Matching Pursuit (SOMP) sparse decomposition orthogonalized fractal encoding was introduced, so that the gray-level matching between blocks was transformed into solving the corresponding sparse coefficient matrix, which realized the matching relationship between one range block and multiple domain blocks. Experimental results show that compared with Sparse Fractal Image Compression (SFIC) algorithm, the proposed algorithm can save about 88% of the encoding time on average without reducing the quality of image reconstruction; compared with the sum of double cross eigenvalues algorithm, the proposed algorithm can significantly shorten coding time while maintaining better reconstruction quality.

A compressed channel sensing method was proposed for Orthogonal Frequency Division Multiplexing (OFDM) based Amplify-and-Forward (AF) cooperative communication network over frequency-selective fading channels. First, by using cyclic matrix theory, the system model was established similar to the traditional point-to-point system model, which consisted of a cascaded channel vector and a measurement matrix. And then, using the theory of compressed sensing, the measurement matrix was proven to satisfy Restricted Isometry Property (RIP) with high probability. Finally, convolution channel impulse response was reconstructed with compressed sensing algorithm. According to the figures example, the cooperative channel exhibited an inherent sparse or sparse clustering structure. Hence, the proposed method can fully exploit the inherent sparse structure in cooperative channel. The simulation results confirm that the proposed method provides significant improvement in Mean Square Error (MSE) performance or spectral efficiency compared with the traditional linear channel estimation methods.

To achieve sign language video coding of region of interest, and improve call efficiency, a fast segmentation methodology of sign language video based on Cellular Neural Network (CNN) was proposed. Firstly, the skin regions of sign language video were detected through corresponding CNN templates by using the skin color information characteristics. Secondly, CNN based motion detection was carried out on the skin detection results by using inter-frame difference algorithm, and then the initial gesture region could be obtained. Finally, morphological processing methods were employed to fill small holes and smooth the boundaries of regions, and eventually the segmentation of the face and hands regions of sign language video image sequence was realized. The results show that the method can rapidly and accurately segment sign language video.