The high efficiency video coding (HEVC) standard provides superior efficiency for encoding and improves the compression ratio by almost 50% compared with previous video coding standards, such as advanced video coding (AVC). However, more intensive computation complexity is introduced by implementing the flexible quad tree-structured coding model. In a typical HEVC encoder, coding units (CUs) in a coding tree unit (CTU) that is built as a quad-tree structure are recursively traversed each depth level (CU size) to select the optimal coding configuration. Therefore, most of the encoding time is spent searching for the optimal coding configuration. In this paper, an efficient and fast CU size decision algorithm is proposed to reduce HEVC encoder complexity by the spatiotemporal features. First, an adaptive depth-range prediction method minimizes the possible range depth level by observing previous frames and proximal CTUs. Second, an early termination method based on the boundary examination from the de-blocking filter (DBF) prevents unnecessary calculation on small CU sizes. Furthermore, according to the sum of absolution difference (SAD), a smooth area detection mechanism is triggered when the predictive depth range excludes the largest CU size. This mechanism increase the bitrate of the CU, which contains static objects with complex textures. Compared with the HM 16, the experimental results revealed that the proposed algorithm can achieve an average 59.73% and 64.98% reduction in encoding time along with a 0.68% and 1.27% Bjontegaard Delta bitrate (BDBR) penalty for various test videos under low-delay P and random-access conditions, respectively.

中文翻译：

---

一种基于时空内容的 HEVC CU 大小决策算法

与之前的视频编码标准（例如高级视频编码 (AVC)）相比，高效视频编码 (HEVC) 标准提供了卓越的编码效率，并将压缩率提高了近 50%。然而，通过实现灵活的四叉树结构编码模型引入了更密集的计算复杂度。在典型的 HEVC 编码器中，构建为四叉树结构的编码树单元 (CTU) 中的编码单元 (CU) 递归遍历每个深度级别（CU 大小）以选择最佳编码配置。因此，大部分编码时间都花在寻找最佳编码配置上。在本文中，提出了一种高效且快速的 CU 大小决策算法，以通过时空特征降低 HEVC 编码器的复杂度。第一的，自适应深度范围预测方法通过观察先前帧和近端 CTU 来最小化可能的范围深度级别。其次，基于去块滤波器 (DBF) 边界检查的提前终止方法可防止对小 CU 大小进行不必要的计算。此外，根据绝对差之和（SAD），当预测深度范围排除最大CU尺寸时，将触发平滑区域检测机制。这种机制提高了 CU 的比特率，其中包含具有复杂纹理的静态对象。与HM 16相比，实验结果表明，该算法可以在低延迟下对各种测试视频实现平均59.73%和64.98%的编码时间减少以及0.68%和1.27%的Bjontegaard Delta比特率（BDBR）惩罚P 和随机访问条件，