Significance Evaluation of Video Data Over Media Cloud Based on Compressed Sensing
Significance Evaluation of Video Data Over Media Cloud Based on Compressed Sensing
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DOI：
10.1109/tmm.2016.2564100
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发表时间：
2016-07
影响因子：
7.3
通讯作者：
Jie Guo;Bin Song;Xiaojiang Du
中科院分区：
文献类型：
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作者：
Jie Guo;Bin Song;Xiaojiang Du
鉴于媒体云和用户之间的通信环境不断变化，需要确保视频的最重要部分能够成功传输。在传统的视频编码方法中，如H.264，虽然有一些技术来评估视频数据的重要性，但评估算法往往是简单和不准确的。提出了一种基于压缩感知的视频数据重要性评价方法。具体来说，我们提出了一种方法，直接通过使用视频数据的测量值来获得训练的字典，然后保留稀疏分量并生成显著性图。由于稀疏分量能够反映视频的本质部分，我们讨论了如何分析显著区域的面积和分布。最后给出了一种计算框架重要度的方法。实验结果表明，提出的显着图反映了人类的焦点。该方法可用于通过“无线”传输分发视频数据，并向移动的用户提供良好的视频质量。
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.