Image-Based Rendering using Point Cloud for 2D Video Compression

H. Golestani, Thibaut Meyer, M. Wien
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引用次数: 2

Abstract

The main idea of this paper is to extract the 3D scene geometry for the observed scene and use it for synthesizing a more precise prediction using Image-Based Rendering (IBR) for motion compensation in a hybrid coding scheme. The proposed method first extracts camera parameters using Structure from Motion (SfM). Then, a Patch-based Multi-View Stereo (PMVS) technique is employed to generate the scene Point-Cloud (PC) only from already decoded key-frames. Since the PC could be really sparse in poorly reconstructed regions, a depth expansion mechanism is also used. This 3D information helps to properly warp textures from the key-frames to the target frame. This IBR-based prediction is then used as an additional reference for motion compensation. In this way, the encoder can choose between the rendered prediction and the regular reference pictures through a rate- distortion optimization. On average, the simulation results show about 2.16% bitrate reduction compared to the reference HEVC implementation, for tested dynamic and static scene video sequences.
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使用点云进行2D视频压缩的基于图像的渲染
本文的主要思想是提取观察场景的三维场景几何形状,并使用基于图像的渲染(IBR)在混合编码方案中进行运动补偿,从而合成更精确的预测。该方法首先利用SfM (Structure from Motion)提取相机参数。然后,采用基于patch的多视点立体(PMVS)技术,仅从已解码的关键帧生成场景点云(PC)。由于PC在重建较差的区域可能非常稀疏,因此还使用了深度扩展机制。这个3D信息有助于正确地将纹理从关键帧扭曲到目标帧。这种基于ibr的预测然后用作运动补偿的附加参考。这样,编码器可以通过率失真优化在渲染的预测图像和常规参考图像之间进行选择。对于测试的动态和静态场景视频序列,仿真结果显示,与参考HEVC实现相比,平均比特率降低了2.16%。
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