High-Efficiency Video Coding provides a better compression ratio compared to earlier standard, H.264/Advanced Video Coding. In fact, HEVC saves 50% bit rate compared to H.264/AVC for the same subjective quality. This improvement is notably obtained through the hierarchical quadtree structured Coding Unit. However, the computational complexity significantly increases due to the full search Rate-Distortion Optimization, which allows reaching the optimal Coding Tree Unit partition. Despite the many speedup algorithms developed in the literature, the HEVC encoding complexity still remains a crucial problem in video coding field. Towards this goal, we propose in this paper a deep learning model-based fast mode decision algorithm for HEVC intermode. Firstly, we provide a deep insight overview of the proposed CNN-LSTM, which plays a kernel and pivotal role in this contribution, thus predicting the CU splitting and reducing the HEVC encoding complexity. Secondly, a large training and inference dataset for HEVC intercoding was investigated to train and test the proposed deep framework. Based on this framework, the temporal correlation of the CU partition for each video frame is solved by the LSTM network. Numerical results prove that the proposed CNN-LSTM scheme reduces the encoding complexity by 58.60% with an increase in the BD rate of 1.78% and a decrease in the BD-PSNR of -0.053 dB. Compared to the related works, the proposed scheme has achieved a best compromise between RD performance and complexity reduction, as proven by experimental results.

中文翻译：

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基于CNN-LSTM学习方法的高效视频编码标准的复杂度降低

与较早的标准H.264 /高级视频编码相比，高效视频编码提供了更好的压缩率。实际上，在相同的主观质量下，HEVC比H.264 / AVC节省了50％的比特率。该改进尤其是通过分层四叉树结构化编码单元获得的。但是，由于完全搜索率失真优化，计算复杂度显着增加，从而可以达到最佳的编码树单元分区。尽管在文献中开发了许多加速算法，但是HEVC编码复杂度仍然是视频编码领域中的关键问题。为了实现这一目标，我们在本文中提出了一种基于深度学习模型的HEVC联模快速模式决策算法。首先，我们对拟议的CNN-LSTM进行了深入的概述，在这一贡献中起核心作用和关键作用，从而预测CU分裂并降低HEVC编码的复杂性。其次，研究了用于HEVC互编码的大型训练和推理数据集，以训练和测试所提出的深度框架。基于此框架，LSTM网络解决了每个视频帧的CU分区的时间相关性。数值结果表明，提出的CNN-LSTM方案将BD速率提高了1.78％，将BD-PSNR降低了-0.053 dB，将编码复杂度降低了58.60％。与相关工作相比，该方案已在RD性能与降低复杂度之间取得了最佳折衷，实验结果证明了这一点。研究了用于HEVC互编码的大型训练和推理数据集，以训练和测试所提出的深度框架。基于此框架，LSTM网络解决了每个视频帧的CU分区的时间相关性。数值结果表明，提出的CNN-LSTM方案将BD速率提高了1.78％，将BD-PSNR降低了-0.053 dB，将编码复杂度降低了58.60％。与相关工作相比，该方案已在RD性能与降低复杂度之间取得了最佳折衷，实验结果证明了这一点。研究了用于HEVC互编码的大型训练和推理数据集，以训练和测试所提出的深度框架。基于此框架，LSTM网络解决了每个视频帧的CU分区的时间相关性。数值结果表明，提出的CNN-LSTM方案将BD速率提高了1.78％，将BD-PSNR降低了-0.053 dB，将编码复杂度降低了58.60％。与相关工作相比，该方案已在RD性能与降低复杂度之间取得了最佳折衷，实验结果证明了这一点。数值结果表明，提出的CNN-LSTM方案将BD速率提高了1.78％，将BD-PSNR降低了-0.053 dB，将编码复杂度降低了58.60％。与相关工作相比，该方案已在RD性能与降低复杂度之间取得了最佳折衷，实验结果证明了这一点。数值结果表明，提出的CNN-LSTM方案将BD速率提高了1.78％，将BD-PSNR降低了-0.053 dB，将编码复杂度降低了58.60％。与相关工作相比，该方案已在RD性能与降低复杂度之间取得了最佳折衷，实验结果证明了这一点。