Deep Inter Prediction via Reference Frame Interpolation for Blurry Video Coding

Abstract

In High Efficiency Video Coding (HEVC), inter prediction is an important module for removing temporal redundancy. The accuracy of inter prediction is much affected by the similarity between the current and reference frames. However, for blurry videos, the performance of inter coding will be degraded by varying motion blur, which is derived from camera shake or the acceleration of objects in the scene. To address this problem, we propose to synthesize additional reference frame via the frame interpolation network. The synthesized reference frame is added into reference picture lists to supply more credible reference candidate, and the searching mechanism for motion candidates is changed accordingly. In addition, to make our interpolation network more robust to various inputs with different compression artifacts, we establish a new blurry video database to train our network. With the well-trained frame interpolation network, compared with the reference software HM-16.9, the proposed method achieves on average 1.55% BD-rate reduction under random access (RA) configuration for blurry videos, and also obtains on average 0.75% BD-rate reduction for common test sequences.