Abstract
The expanding social network and multimedia technologies encourage more and more people to store and transmit information in visual format, such as image and video. However, the cost of this convenience brings about a shock to traditional video severs and exposes them under the risk of overloading. In the huge volume of online videos, there are a large amount of near-duplicate videos (NDVs). Although quite a number of research work have been proposed to detect NDVs, little research effort is made to compress these NDVs in a more effective manner than independent video compression. In this study, we make an in-depth exploration of the data redundancy of NDVs and propose a video analysis and coding framework to jointly compress NDVs. In order to employ the proposed NDV analysis and coding framework, a graph-based similar video grouping method and a number of preprocessing functions are designed to explore the correlation of visual information among NDVs and thus suit the requirement of joint video coding. Experimental results verify that the proposed NDV analysis and coding framework is able to effectively compress NDVs and thus save video data storage.
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