Multidirectional parabolic prediction-based interpolation-free sub-pixel motion estimation

•Multidirectional parabolic prediction patterns are developed to reduce coding loss.•Projection is introduced to improve each directional parabolic prediction pattern.•A prediction enhancement mechanism is presented by incorporating AMVP information.•Significant complexity reduction (about 90%) of sub-pixel ME is achieved over HM16.0.•Proposed algorithm outperforms state-of-the-art algorithms with improved coding performance.

Motion estimation is one of the most effective key components in video codecs to eliminate temporal redundancies between successive frames. Sub-pixel motion estimation, including sub-pixel interpolation and sub-pixel motion vector prediction, has been employed in many recent international video coding standards to further improve the rate-distortion performance. Although sub-pixel motion estimation plays an important role in improving coding efficiency, it simultaneously brings significant computational complexity, especially in sub-pixel interpolation and motion vector prediction. In this paper, we propose a multidirectional parabolic prediction-based interpolation-free sub-pixel motion estimation scheme. First, four different directional parabolic prediction patterns are developed to estimate the candidate optimal sub-pixel positions, making full use of the available rate-distortion costs of neighboring positions in the integer-pixel motion estimation. Second, an improvement for each directional parabolic prediction pattern is realized by projection operations to generate a more precise candidate optimal sub-pixel position. Third, an optimal sub-pixel position decision method is presented to select the most likely predictive position from the four candidate sub-pixel positions derived in the previous steps. Finally, by fully reusing the information of the powerful advanced motion vector prediction (AMVP) in HEVC, a prediction enhancement mechanism is presented in which the final optimal sub-pixel motion vector (MV) is derived between the MV corresponding to the most likely predictive position and the predictive motion vector (PMV), without requiring extra interpolation operations. Experimental results show that the proposed algorithm significantly reduces the computational complexity of sub-pixel motion estimation and outperforms state-of-the-art interpolation-free sub-pixel motion estimation algorithms with much improved encoding performance and comparable computational complexity reduction.