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Region of interest based scalable image and video coding a superlative study

机译:基于兴趣区域的可伸缩图像和视频编码的最高级研究

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A video consists of many number of frames that contains the valuable information in different ways. Region of interest depends on what we want to extract from the different frames of the video which is application oriented. If for example if one is interested to detect the face of a person from a video then the Region of Interest (ROI) would be the face only. ROI scalability is of greater importance now a days in the field like video surveillance and hand held devices, like where some visual data is more significant and interesting than the non ROI parts of the video. ROI mainly operates on any image and video coding which boosts the regions exclusively which is more important and to have high visual quality. The main aim of SVC is to produce all the three basic scalabilities like temporal, spatial and quality scalabilities of an encoded bit stream. The ROI consists of a region which is important to a particular user with heterogeneous display devices. By doing so, less bandwidth is required for transmission as compared to total region is transmitted or decoded. In this paper we are going to extract the required ROI from the frames of a video sequence and carry out the coding process for Scalability.
机译:视频由许多帧组成,这些帧以不同的方式包含有价值的信息。感兴趣的区域取决于我们要从面向应用程序的视频的不同帧中提取什么。例如,如果某个人有兴趣从视频中检测某人的面部,则感兴趣区域(ROI)将仅是面部。如今,在诸如视频监控和手持设备之类的领域中,ROI的可伸缩性已经变得越来越重要,例如某些视频数据比视频的非ROI部分更为重要和有趣。 ROI主要对任何图像和视频编码进行操作,从而专门增强了更重要的区域并具有较高的视觉质量。 SVC的主要目的是产生所有三个基本可伸缩性,例如编码位流的时间,空间和质量可伸缩性。 ROI由对于使用异构显示设备的特定用户而言很重要的区域组成。这样,与传输或解码整个区域相比,传输所需的带宽更少。在本文中,我们将从视频序列的帧中提取所需的ROI,并进行可扩展性的编码过程。

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