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首页> 外文期刊>IEEE Journal on Selected Areas in Communications >Video representation with three-dimensional entities
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Video representation with three-dimensional entities

机译:具有三维实体的视频表示

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摘要

Very low bit-rate coding requires new paradigms that go well beyond pixel- and frame-based video representations. We introduce a novel content-based video representation using tridimensional entities: textured object models and pose estimates. The multiproperty object models carry stochastic information about the shape and texture of each object present in the scene. The pose estimates define the position and orientation of the objects for each frame. This representation is compact. It provides alternative means for handling video by manipulating and compositing three-dimensional (3-D) entities. We call this representation tridimensional video compositing, or 3DVC for short. We present the 3DVC framework and describe the methods used to construct incrementally the object models and the pose estimates from unregistered noisy depth and texture measurements. We also describe a method for video frame reconstruction based on 3-D scene assembly, and discuss potential applications of 3DVC to video coding and content-based handling. 3DVC assumes that the objects in the scene are rigid and segmented. By assuming segmentation, we do not address the difficult questions of nonrigid segmentation and multiple object segmentation. In our experiments, segmentation is obtained via depth thresholding. It is important to notice that 3DVC is independent of the segmentation technique adopted. Experimental results with synthetic and real video sequences where compression ratios in the range of 1:150-1:2700 are achieved demonstrate the applicability of the proposed representation to very low bit-rate coding.
机译:极低的比特率编码需要新的范例,远远超出了基于像素和帧的视频表示形式。我们介绍了一种使用三维实体的新颖的基于内容的视频表示形式:纹理对象模型和姿势估计。多属性对象模型携带有关场景中每个对象的形状和纹理的随机信息。姿势估计定义每个帧的对象的位置和方向。这种表示是紧凑的。它提供了通过操纵和合成三维(3-D)实体来处理视频的替代方法。我们称此表示为三维视频合成,简称3DVC。我们提出了3DVC框架,并描述了用于从未注册的噪声深度和纹理测量值逐步构建对象模型和姿态估计的方法。我们还描述了一种基于3-D场景组合的视频帧重建方法,并讨论了3DVC在视频编码和基于内容的处理中的潜在应用。 3DVC假定场景中的对象是刚性的并且是分段的。通过假设分割,我们不会解决非刚性分割和多对象分割的难题。在我们的实验中,分割是通过深度阈值获得的。重要的是要注意3DVC与采用的分割技术无关。合成和真实视频序列的实验结果(压缩比在1:150-1:2700范围内实现)证明了所提出的表示形式可用于非常低的比特率编码。

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