To address the issue of existing information diffusion models overlooking user subjectivity and social network dynamics， an SCBRD （Susceptible-Commented-Believed-Recovered-Defensed） opinion propagation model that considers user initiative and mobility in heterogeneous networks was proposed.Firstly， the basic reproduction number was determined using the next-generation matrix method， and the system’s dynamics and optimal control were investigated by applying Lyapunov’s stability theorem and Pontryagin’s principle. Then， a simulation analysis was performed based on BA （Barabási-Albert） scale-free network to identify the significant factors affecting the opinion propagation. The results reveal that users’ curiosity， forwarding behavior， and admission rate play dominant roles in information diffusion and the system has an optimal control solution. Finally， the model’s rationality was validated based on actual data. Compared to the SCIR （Susceptible-inCubation-Infective-Refractory） model， the SCBRD model improves fitting accuracy by 27.40% and reduces the Root Mean Square Error （RMSE） of prediction by 39.02%. Therefore， the proposed model can adapt to the complex and changing circumstances of information diffusion and provide better guidance for official public opinion regulation.

To better evaluate image quality, the authors proposed a top-down no-reference image objective quality evaluation method based on the research of human visual model and human visual characteristics. The new method divided the target image into segments and calculated every segment in different color channel, then output the average visual property values. It made evaluation model not only be consistent with the advantage of objective quality assessment, but also took into account the human visual experience. It was consistent with the objectivity and subjectivity of human eyes. The validated results of image subjective evaluation on database of TEXAS University of United States prove that the proposed method gets better consistency with subjective assessment.

In this paper, an adaptive color image enhancement method was proposed: Firstly, color image was transformed from RGB to HSV color space and the H component was kept invariable, while the illumination component of brightness image could be estimated through Adaptive Bidimensional Empirical Mode Decomposition (ABEMD); Secondly, reflection component was figured out by the method of center/surround Retinex algorithm, and the illumination and reflection components were controlled through Gamma emendation and Weber's law and processed with weighted average method; Thirdly, the S component was adjusted adaptively based on characteristics of the whole image, and then image was transformed back to RGB color space. The method could be evaluated by subjective effects and objective image quality assessment, and the experiment results show that the proposed algorithm is better in mean value, square variation, entropy and resolution than MSR algorithm and Meylan's algorithm.