Complementary Motion Vector for Motion Prediction in Video Coding with LongTerm Reference In HEVC, there are two types of motion vector (MV) when longterm reference is enabled: shortterm MV (SMV) pointing to shortterm reference frame and longterm MV (LMV) pointing to longterm reference frame. And crossclass prediction between SMV and LMV is not allowed because of their low correlation. Therefore, MV predictor candidates of current block would be inadequate when neighboring MVs are in different types. This paper proposes a complementary MV to enrich MV predictor candidates for current block. There would be two types of MV for each neighboring inter block. In addition to MV used in motion compensation, a complementary MV of the other type is derived by reconstructed pixels. This paper also proposed a reliabilitybased MV predictor candidate list construction method to improve the prediction efficiency of complementary MVs. Experimental results show that the proposed method can achieve 1.18% coding performance improvement on average.

Reference Picture Synthesis for Video Sequences Captured with a Monocular Moving Camera Interframe prediction plays an important role in video coding by predicting the current frame from previously encoded pictures, called reference pictures. In the case of camera motion, the content of a current frame could be very different from its reference pictures and may consequently lead to a more difficult Motion Compensation (MC). The main idea of this paper is to process the input 2D video sequence in order to estimate the 3D geometry of the scene and then employ this data to virtually synthesize geometrically compensated reference pictures. Since these virtual reference pictures are more similar to the current frame, motion estimation and consequently coding efficiency could be enhanced. The proposed method is tested over six different video sequences and around 11% bitrate reduction is achieved compared to the High Efficiency Video Coding (HEVC) standard.