Neural Networks Based Fractional Pixel Motion Estimation for HEVC

High Efficiency Video Coding (HEVC) provides more compression than its predecessors. One of the modules that contributes to higher compression rates is the Motion Estimation module, which consists of Integer and Fractional pixel motion estimation. The Fractional Motion Estimation (FME) process performs interpolations to find sample values at fractional-pixel locations, which can be computationally demanding. In this paper, we propose an interpolation-free method for FME based on Artificial Neural Networks (ANNs). Our proposed method is implemented in HEVC reference software (HM-16.9). According to our results, ANNs can accomplish FME task with an average increase of 2.6% in BDRate and an average reduction of 0.09 dB in BD-PSNR.