An Efficient Four-Parameter Affine Motion Model for Video Coding

TL;DR

This paper introduces a simplified four-parameter affine motion model for video coding that effectively handles complex motions while reducing computational complexity and bit usage.

Contribution

The paper proposes a novel four-parameter affine motion model, along with efficient encoding and compensation algorithms, improving motion representation and coding efficiency in video compression.

Findings

Achieves 11.1% and 19.3% bit savings in different configurations.

Handles complex natural video motions like rotation and zooming.

Maintains acceptable computational complexity.

Abstract

In this paper, we study a simplified affine motion model based coding framework to overcome the limitation of translational motion model and maintain low computational complexity. The proposed framework mainly has three key contributions. First, we propose to reduce the number of affine motion parameters from 6 to 4. The proposed four-parameter affine motion model can not only handle most of the complex motions in natural videos but also save the bits for two parameters. Second, to efficiently encode the affine motion parameters, we propose two motion prediction modes, i.e., advanced affine motion vector prediction combined with a gradient-based fast affine motion estimation algorithm and affine model merge, where the latter attempts to reuse the affine motion parameters (instead of the motion vectors) of neighboring blocks. Third, we propose two fast affine motion compensation algorithms. One is the one-step sub-pixel interpolation, which reduces the computations of each interpolation. The other is the interpolation-precision-based adaptive block size motion compensation, which performs motion compensation at the block level rather than the pixel level to reduce the interpolation times. Our proposed techniques have been implemented based on the state-of-the-art high efficiency video coding standard, and the experimental results show that the proposed techniques altogether achieve on average 11.1% and 19.3% bits saving for random access and low delay configurations, respectively, on typical video sequences that have rich rotation or zooming motions. Meanwhile, the computational complexity increases of both encoder and decoder are within an acceptable range.