Significance Evaluation of Video Data over Media Cloud Based on Compressed Sensing

Given the varying communication environment between the media cloud and users, there is a need to ensure the most significant part of a video will be successfully transmitted. Although there exist some techniques to evaluate the significance of video data in traditional video coding methods, such as H.264, the evaluation algorithms are often simple and inaccurate. This paper presents a novel significance evaluation method for video data based on compressed sensing. Specifically, we propose a method to obtain a trained dictionary directly by using the measurements of the video data, and then keep the sparse components and generate a saliency map. Since the sparse components can reflect the essential parts of videos, we discuss how to analyze the area and distribution of salient regions. At last, we present a computing method that gives the degree of significance of a frame. Experimental results show that the proposed saliency map reflects the focus points of humans. The method can be used in the distribution of video data over 'wireless' transmissions and provide good video quality to mobile users.