An Adjustable Fast Decision Method for Affine Motion Estimation in VVC

The Affine motion estimation (AME) in Versatile Video Coding (VVC) can predict complex non-translational motions such as rotation, zoom, or shearing more effectively than the translational motion estimation (TME) tools, at the cost of greatly increased computational complexity. In this paper, to reduce encoding complexity, we propose a novel adjustable fast decision method for AME in VVC. Our method skips the AME process for blocks with low TME rate-distortion (RD) cost as we observed that skipping them reduces the encoding time without significantly affecting the compression performance. A distinctive feature of the proposed method is that it can be progressively adjusted to provide different compromises between speed-up and compression performance. First, a default TME RD cost threshold is estimated using a Multiple Linear Regression (MLR) model and then, adjusted to achieve the desired trade-off between speed-up and coding performance. Experimental results show that, with the default threshold, the proposed method can reduce the VTM encoding time by 8% on average, on classes B, C, and D, with a Bjøntegaard-Delta bitrate (BD-BR) of 0.44%. For the same classes and using 1.5 times the default threshold, it can reach 11% with a BD-BR of 0.82%.