The fundamental motion model of the conventional block-based motion compensation in High Efficiency Video Coding (HEVC) is a translational motion model. However, in the real world, the motion of an object exists in the form of combining many kinds of motions. In Versatile Video Coding (VVC), a block-based 4-parameter and 6-parameter affine motion compensation (AMC) is being applied. In natural videos, in the majority of cases, a rigid object moves without any regularity rather than maintains the shape or transform with a certain rate. For this reason, the AMC still has a limit to compute complex motions. Therefore, more flexible motion model is desired for new video coding tool. In this paper, we design a perspective affine motion compensation (PAMC) method which can cope with more complex motions such as shear and shape distortion. The proposed PAMC utilizes perspective and affine motion model. The perspective motion model-based method uses four control point motion vectors (CPMVs) to give degree of freedom to all four corner vertices. Besides, the proposed algorithm is integrated into the AMC structure so that the existing affine mode and the proposed perspective mode can be executed adaptively. Because the block with the perspective motion model is a rectangle without specific feature, the proposed PAMC shows effective encoding performance for the test sequence containing irregular object distortions or dynamic rapid motions in particular. Our proposed algorithm is implemented on VTM 2.0. The experimental results show that the BD-rate reduction of the proposed technique can be achieved up to 0.45% and 0.30% on Y component for random access (RA) and low delay P (LDP) configurations, respectively.
This paper introduces a globally optimal algorithm for obtaining the rotational displacement between the coordinate frames of a rotation sensor and a camera that are rigidly attached. Our method minimizes the geometrically meaningful error using a branch-and-bound algorithm to find the global solution. For this, we derive a bounding inequality and corresponding feasibility problem for a top-down efficient search over the rotation space to minimize the L1-, L2-, or L∞-norm error function. Experiments are performed with synthetic and real data sets to show the efficacy of the algorithm.
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