Convolutional neural network based low complexity HEVC intra encoder

Abstract

Video coding is one of the key technologies of visual sensors. As the state-of-art video coding standard, High Efficiency Video Coding (HEVC) achieves a significant high compression ratio for video. However, it also introduces heavy computational complexity, leading to challenges in application of visual sensors. To reduce the complexity of HEVC intra encoder, this paper proposed a one-stage decision method of CU/PU partition and prediction mode for intra coding. First, the potential factors that may related to the corresponding decisions in CU/PU are explored. Based on this, a one-stage decision network (OSDN) structure is specially designed to determine these decisions. Consequently, the complexity of HEVC intra coding can be drastically reduced by avoiding the brute-force search. Then, OSDN is embedded into the HEVC reference software HM 15.0. Thresholds are set to let the encoder switch between OSDN and the original implementation in HEVC to obtain the final decisions. The experimental results show that the proposed method can reduce 73.69% intra encoding time with 0.1673 dB BD-PSNR loss on average. In addition, the trade-off between RD performance degradation and complexity reduction can be controlled by thresholds.