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首页> 外文期刊>IEEE Transactions on Circuits and Systems for Video Technology >Global motion estimation in model-based image coding by trackingthree-dimensional contour feature points
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Global motion estimation in model-based image coding by trackingthree-dimensional contour feature points

机译：跟踪三维轮廓特征点的基于模型的图像编码全局运动估计

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摘要

A video coding method called model-based image coding has attracted much attention as a potential candidate for low bit-rate visual communication services. This technique reconstructs the facial image with a preknown 3D human face model and its received model motion parameters. The parameters of the head motion are mainly divided into two parts: global motion parameters describe the rigid movement of the head, such as rotation and translation, and local motion parameters which deal with the nonrigid movements of facial expressions, such as the opening and closing of the mouth and eyes. In this paper, we propose a new approach which can estimate the head global motion more robustly and accurately. Comparing with the existing techniques to match only a few key points, here we extract 3-D contour feature points and use chamfer distance matching to estimate head global motion. This can improve and enhance the contour tracking performance greatly. We also develop another technique called facial normalization transform. It maps the facial region of the current input frame back to the normalized pose of the initial frame. Using this transform, we can analyze facial expressions at the same orientation and fixed region. This simplifies the analysis work a lot. Then, we do our encoding by the clip-and-paste method along with adaptive codebook technique. In the following the coder and decoder system are briefly described
机译：一种称为基于模型的图像编码的视频编码方法作为低比特率视觉通信服务的潜在候选者已经引起了广泛的关注。该技术使用已知的3D人脸模型及其接收的模型运动参数重建人脸图像。头部运动的参数主要分为两部分：全局运动参数描述了头部的刚性运动，例如旋转和平移；以及局部运动参数，用于处理面部表情的非刚性运动，例如打开和关闭。的嘴和眼睛。在本文中，我们提出了一种新的方法，可以更可靠，更准确地估计头部的全局运动。与仅匹配几个关键点的现有技术相比，这里我们提取3-D轮廓特征点，并使用倒角距离匹配来估计头部整体运动。这可以大大改善和增强轮廓跟踪性能。我们还开发了另一种称为面部归一化转换的技术。它将当前输入帧的面部区域映射回初始帧的标准化姿势。使用此变换，我们可以分析相同方向和固定区域的面部表情。这大大简化了分析工作。然后，我们通过剪贴和自适应码本技术一起进行编码。下面简要介绍编码器和解码器系统

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