A Temporal Filtering Approach Based on Optical Flow Estimation for Video Coding
Bohan Li, Lauren Partin, Jingning Han, Yaowu Xu · 2021
Video coding uses motion compensated prediction to exploit temporal correlations for compression efficiency. Prior works have demonstrated that substantial coding gains can be achieved by decomposing a long-term reference frame into a synthetic reference-only (non-displayable) frame and an overlay displayable frame that resembles the original frame. The source of the reference-only frame is typically generated by temporal filtering along the motion trajectories across nearby frames, where the motion trajectories are built using block matching algorithms (BMAs), thereby reducing the noise level within this synthetic frame. Noting that the efficacy of the conventional BMAs are limited to capturing translational motion activities, this paper proposes a novel approach that uses a per-pixel motion field generated by an optical flow estimation to form the motion trajectory for more efficient temporal filtering. It is experimentally shown that the proposed method better captures non-translational motion activities, which translates into considerable coding gains for video signals with such complicate motion patterns.