Deep Neural Network Based Next-Frame Prediction in HEVC Video Sequence

High Efficiency Video Coding (HEVC) is a video compression standard that compresses video sequences with 50% less bit rate compared to ancestor H.264 standard. In HEVC, the motion compensation block utilizes the motion vectors to generate the motion compensated frame. The motion vectors are generated using the motion estimation process that improves the efficiency of HEVC at the expense of high computational complexity. The next-frame prediction technique can be used to predict the motion compensated frame. This paper proposes the next-frame prediction model that predicts the next frame using the previous five frames. The experimental results show that the proposed method achieves a Peak Signal-to-Noise Ratio(PSNR) of 29.35dB with a mean square error loss of 0.003 for the UCF101 dataset video sequence, which is better than state-of-the-art methods.