Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation

Abstract

This study proposes a lightweight solution to estimate affine parameters in affine motion compensation. Most of the current approaches start with an initial approximation based on the standard motion estimation, which only estimates the translation parameters. From there, iterative methods are used to find the best parameters, but they require a significant amount of time. The proposed method aims to speed up the process in two ways, first, skip evaluating affine prediction when it is likely to bring no encoding efficiency benefit, and second, by estimating better initial values for the iteration process. We use the optical flow between the reference picture and the current picture to estimate quickly the best encoding mode and get a better initial estimation. We achieve a reduction in encoding time over the reference of half when compared to the state of the art, with a loss in efficiency below 1%.

Reducing Video Coding Complexity Based on CNN-CBAM in HEVC

“…Therefore, reducing the complexity of quad-tree CU partitioning without compromising coding efficiency is a much-anticipated problem in the field of video compression. Currently, complexity reduction methods are mainly categorized into heuristics and learning-based methods, and there are also individual papers that have proposed other ways to reduce the coding time, like in [5,6].…”