目的 为了提升高效视频编码（HEVC）的编码效率，使之满足高分辨率、高帧率视频实时编码传输的需求。由分析可知帧内编码单元（CU）的划分对HEVC的编码效率有决定性的影响，通过提高HEVC的CU划分效率，可以大大提升HEVC编码的实时性。方法 通过对视频数据分析发现，视频数据具有较强的时间、空间相关性，帧内CU的划分结果也同样具有较强的时间和空间相关性，可以利用前一帧以及当前帧CU的划分结果进行预判以提升帧内CU划分的效率。据此，本文给出一种帧内CU快速划分算法，先根据视频相邻帧数据的时间相关性和帧内数据空间相关性初步确定当前编码块的编码树单元（CTU）形状，再利用前一帧同位CTU平均深度、当前帧已编码CTU深度以及对应的率失真代价值决定当前编码块CTU的最终形状。算法每间隔指定帧数设置一刷新帧，该帧采用HM16.7模型标准CU划分以避免快速CU划分算法带来的误差累积影响。结果 利用本文算法对不同分辨率、不同帧率的视频进行测试，与HEVC的参考模型HM16.7相比，本文算法在视频编码质量基本不变，视频码率稍有增加的情况下平均可以节省约40%的编码时间，且高分辨率高帧率的视频码率增加幅度普遍小于低分辨率低帧率的视频码率。结论 本文算法在HEVC的框架内，利用视频数据的时间和空间相关性，通过优化帧内CU划分方法，对提升HEVC编码，特别是提高高分辨率高帧率视频HEVC编码的实时性具有重要作用。

Objective The coding rate of high efficiency video coding (HEVC) can be reduced by approximately 50% compared with H.264/AVS, with nearly the same video coding quality. However, the coding complexity of HEVC increases exponentially at the same time. In particular, high resolution and high frame rate videos require additional coding time for HEVC. The coding time of HEVC must be reduced to satisfy the requirements of real-time coding and transmission for high-resolution and high frame rate videos. Statistics show that intra-coding unit (CU) segmentation comprises approximately 99% of the total coding time in HEVC, and the efficiency of the CU segmentation has a decisive impact on the efficiency of HEVC. The real-time coding of HEVC can be promoted significantly by optimizing the CU segmentation method used in HEVC. Many methods, such as reducing the traversal range of the depth of the CU, decreasing the rate-distortion cost calculation, and skipping the intra prediction of large CU, have been used in optimizing the CU segmentation method used in HEVC.Method A robust spatial and temporal correlation exists among consecutive frames in video data. Coding tree unit (CTU) exhibits a strong correlation with the CTU of the same position in the consecutive frames and the surrounding CTUs in the same frame in HEVC. According to statistics, approximately 71.5% of CTUs of the same position in the current and consecutive frames provide the same depth, and the correlation between the consecutive frames in the gentle video is stronger than in the dramatic video. Therefore, the CTU of the current frame can be estimated based on the CTUs of its previous frame. The rate-distortion cost ratio between the CTUs of the same position in the consecutive frames is mostly between 0.8 and 1.2. Statistics denotes that the rate-distortion cost ratio is near 1.0 for gentle videos and far from 1.0 for dramatic videos. The rate-distortion value of the current CTU can be estimated based on the rate-distortion value of the CTUs of the same position in the previous frames, which can be used to accelerate CU segmentation. According to the above characteristics, a fast intra CU splitting algorithm is proposed in this paper. In this algorithm, the CTU is determined preliminarily based on the CTU of the same position in the previous frame and its adjacent CTUs. The CTU is finally determined based on the average depth of the CTU of the same position in the previous frame, weighted average depth of its adjacent CTUs, standard deviation of the brightness of CU, and corresponding rate-distortion cost value. All parameters used in the algorithm are obtained according to the actual video. The proposed method can significantly reduce the intra CU splitting time. The selected refresh frame adopts the standard CU partition method used in HM16.7 to avoid errors caused by the cumulative effects in this fast CU splitting algorithm at the interval-specified frames in the video. All the codes of the proposed algorithm were written in C++ based on the HM16.7, which is a popular framework for HEVC.Result The proposed method was used in many different resolution and frame rate videos to verify the feasibility and reliability of the method. Experimental results show that this algorithm can maintain the video quality and save approximately 40% encoding time with only nearly 1.4% increase in video coding rate, approximately 2.93% increase in the BDBR (bjøntegaard delta bitrate) of a video, and approximately 0.17 dB decrease in the BD-PSNR (bjøntegaard delta peak signal-to-noise rate) compared with HM16.7. The statistical results indicate that the absolute values of BDBR and BD-PSNR have a decreasing trend with the increase in video resolution, and the increment in video coding rate for high-resolution, high frame rate videos is generally smaller than that for low-resolution, low-frame rate videos. Conclusion The analysis of the experimental results shows that the proposed algorithm based on HEVC framework HM16.7 can reduce the video coding time by using the spatial and temporal correlation in video data to decrease the time used for intra-CU splitting. The algorithm skips the rate-distortion calculation of the CU with zero depth and uses the similarity of the CTUs in consecutive frames to determine the CTUs of the current frame in advance. The method is feasible, reliable, and can improve the real-time performance of HEVC significantly, especially for the highresolution, high frame rate videos. The proposed algorithm is more suitable for high-resolution and high frame rate videos and has a better effect for all I-frame encoding schemes than for low-latency and random-access encoding schemes. The proposed algorithm should be continuously optimized to achieve minimal coding time, reduced coding rate, and enhanced coding quality not only for low-resolution and lowframe rate videos but also for different HEVC coding modes.