Nowadays, real-time multimedia applications mandate high video quality while maintaining reasonable bitrates. The H.264 coding delivered inexpensive bitrate costs compared to other coding schemes while maintaining high-grade video quality, yet bounded to deliver higher qualities. Later, High-Efficiency Video Coding (HEVC) improved on H.264 by providing higher video qualities with an efficient bitrate. However, such improvement obligates higher computational expenses due to employing superior techniques like quad-tree for coding tree unit (CTU) partitioning. This paper proposes a framework, named CtuNet, for CTU partitioning by approximating its functionality using deep learning techniques. A ResNet18-CNN model is adopted to predict the CTU partition of the HEVC standard. We have baselined our suggestion with state-of-the-art approaches. The results demonstrate the supremacy of the proposed CtuNet over the other approaches. The CtuNet framework maintains near-optimal results by reducing computational complexity up to $63.68\%$ with negligible degradation in bitrate by $1.77\%$ at intra-prediction.

如今，实时多媒体应用要求高视频质量，同时保持合理的比特率。与其他编码方案相比，H.264 编码提供了低廉的比特率成本，同时保持了高品质的视频质量，但必定会提供更高的质量。后来，高效视频编码 (HEVC) 通过以高效的比特率提供更高的视频质量，对 H.264 进行了改进。然而，由于采用四叉树等高级技术进行编码树单元 (CTU) 分区，这种改进需要更高的计算开销。本文提出了一个名为 CtuNet 的框架，通过使用深度学习技术近似其功能来进行 CTU 分区。采用 ResNet18-CNN 模型预测 HEVC 标准的 CTU 分区。我们已经使用最先进的方法将我们的建议作为基线。结果证明了所提出的 CtuNet 优于其他方法。CtuNet 框架通过将计算复杂度降低到接近最优的结果$63.68\%$ 比特率的下降可以忽略不计 $1.77\%$ 在帧内预测。