In modern video coding standards, block-based inter prediction is widely adopted, which brings high compression efficiency. However, in natural videos, there are usually multiple moving objects of arbitrary shapes, resulting in complex motion fields that are difficult to compactly represent. This problem has been tackled by more flexible block partitioning methods in the Versatile Video Coding (VVC) standard, but the more flexible partitions require more overhead bits to signal and still cannot be made arbitrary shaped. To address this limitation, we propose an object segmentation-assisted inter prediction method (SAIP), where objects in the reference frames are segmented by some advanced technologies. With a proper indication, the object segmentation mask is translated from the reference frame to the current frame as the arbitrary-shaped partition of different regions without any extra signal. Using the segmentation mask, motion compensation is separately performed for different regions, achieving higher prediction accuracy. The segmentation mask is further used to code the motion vectors of different regions more efficiently. Moreover, segmentation mask is considered in the joint rate-distortion optimization for motion estimation and partition estimation to derive the motion vector of different regions and partition more accurately. The proposed method is implemented into the VVC reference software, VTM version 12.0. Experimental results show that the proposed method achieves up to 1.98%, 1.14%, 0.79%, and on average 0.82%, 0.49%, 0.37% BD-rate reduction for common test sequences, under the Low-delay P, Low-delay B, and Random Access configurations, respectively.

通过使用先进技术对参考帧中的对象进行分割并将分割结果作为不同区域的任意形状分区，我们提出了一种对象分割辅助的帧间预测方法。通过该方法，可以实现更高的预测准确性和更高效的运动向量编码，同时在运动估计和分区估计的联合率失真优化中考虑分割结果，从而更准确地得出了不同区域的运动向量和分区。在实验中，我们发现该方法在常见测试序列中可以分别达到1.98％，1.14％和0.79％的BD速率降低，对应平均的BD速率降低率分别为0.82％，0.49％和0.37％，在低延迟P、低延迟B和随机存取配置下。

基于物体分割辅助的通用视频编码帧间预测