Affine Motion Prediction Based on Translational Motion Vectors

TLDR

Motion compensated prediction drives compression in video coding standards such as H.26X and MPEG-X, but translational motion vectors inadequately model complex motion, prompting the use of polynomial or affine models. The study proposes a novel affine predictor stage that can be easily integrated into current codecs to improve motion compensated prediction quality. The authors implement the predictor as an affine motion model that replaces or refines translational vectors within the coding pipeline. Using the affine predictor passively yields up to 0.7 dB and 1.6 dB improvements on the Mobil and Flower Garden sequences, and refining translational MVs with the affine model further boosts gains to 0.98 dB and 1.88 dB respectively.

Abstract

In all of the video coding standards like H.26X and MPEG-X, much of the compression comes from motion compensated prediction (MCP). Translational motion vectors (MVs) poorly model complex motion and thus coders using polynomial or affine MVs have been proposed in the past. In this paper, we demonstrate a novel affine predictor stage which can be easily incorporated into current codecs greatly increasing MCP quality. If used passively to generate the final prediction, gains of up to 0.7 and 1.6 dB were easily realized for ldquomobilerdquo and ldquoflower gardenrdquo video sequences, respectively. In addition, when the translational MVs are refined, gains of up to 0.98 and 1.88 dB for ldquomobilerdquo and ldquoflower gardenrdquo video sequences were respectively realized.