Motion saliency detection in a compressed domain is crucial for various video applications, including retargeting, surveillance, object checking, and segmentation. The goal of this paper is to improve the performances of an existing motion saliency detection model in a compressed domain developed by Fang et al. Specifically, we improve the detection accuracy of motion center-surround features by dynamically fitting the parameters of a Gaussian distribution model. Besides, the parameters for the distribution of distance in horizontal and vertical directions are obtained separately instead of treating them together. In addition, the motion importance features are exploited to strengthen the performance of detection. Experimental results demonstrate that the proposed motion saliency detection method outperforms the existing approaches in both a pixel and compressed domain.
In order to overcome the impacts on Space Optical Communication System brought by the turbulent in the atmosphere, the multi-transmitting technology, i.e. the space diversity technology is adopted in Space Optical Communication System to improve its properties at a lower cost such as reducing the error rate and raising its reliabilities. However, with the increase of correlations among the diversity signals, the validities of space diversity technologies decrease. On this condition, the adaptive diversity technology can be introduced to elevate the signal-to-noise rate. Under the condition of receiving correlative signals, with the help of combining selection diversity with adaptive signal processing technology, in this paper the SD-LMS (Selection Diversity-Least Mean Square) adaptive signal-processing algorithm is advanced. The following result could be obtained through the theoretical analysis: the signal-to-noise of outputting signals will be raised with the increase of the numbers of the diversity inputting signals, and the outputting signal distortion is similar to that of the single-way inputting signals. The simulating experiments show that SD-LMS adaptive signal-processing algorithm can significantly improve the properties of the outputting signals. The most prominent characteristics of the SD-LMS adaptive signal-processing algorithm is that it can automatically adjust the optimal weight factors of the adaptive filtering, and adapt itself to any changes of the noise without specially evaluating the properties of channels and noise.