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Estimation of depth fields suitable for video compression based on 3-D structure and motion of objects

机译:基于3-D结构和物体运动的适用于视频压缩的深度场估计

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摘要

Intensity prediction along motion trajectories removes temporal redundancy considerably in video compression algorithms. In three-dimensional (3-D) object-based video coding, both 3-D motion and depth values are required for temporal prediction. The required 3-D motion parameters for each object are found by the correspondence-based E-matrix method. The estimation of the correspondences-two-dimensional (2-D) motion field-between the frames and segmentation of the scene into objects are achieved simultaneously by minimizing a Gibbs energy. The depth field is estimated by jointly minimizing a defined distortion and bit-rate criterion using the 3-D motion parameters. The resulting depth field is efficient in the rate-distortion sense. Bit-rate values corresponding to the lossless encoding of the resultant depth fields are obtained using predictive coding; prediction errors are encoded by a Lempel-Ziv algorithm. The results are satisfactory for real-life video scenes.
机译:沿运动轨迹的强度预测在视频压缩算法中大大消除了时间冗余。在基于三维(3-D)对象的视频编码中,时间预测需要3-D运动和深度值。每个对象所需的3-D运动参数可通过基于对应关系的E矩阵方法找到。通过最小化吉布斯能量,可以同时估计帧之间的对应二维(2-D)运动场并将场景分割为对象。通过使用3-D运动参数共同最小化定义的失真和比特率标准,可以估算深度场。所得的深度场在速率失真的意义上是有效的。使用预测编码获得与所得深度场的无损编码相对应的比特率值;预测误差由Lempel-Ziv算法编码。对于真实视频场景,结果令人满意。

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