The High Efficiency Video Coding (HEVC) is adopted by various video applications in recent years. Because of its high computational demand, controlling the complexity of HEVC is of paramount importance to appeal to the varying requirements in many applications, including power-constrained video coding, video streaming, and cloud gaming. Most of the existing complexity control methods are only capable of considering a subset of the decision space, which leads to low coding efficiency. While the efficiency of machine learning methods such as Support Vector Machines (SVM) can be employed for higher precision decision making, the current SVM-based techniques for HEVC provide a fixed decision boundary which results in different coding complexities for different video content. Although this might be suitable for complexity reduction, it is not acceptable for complexity control. This paper proposes an adjustable classification approach for Coding Unit (CU) partitioning, which addresses the mentioned problems of complexity control. Firstly, a novel set of features for fast CU partitioning is designed using image processing techniques. Then, a flexible classification method based on SVM is proposed to model the CU partitioning problem. This approach allows adjusting the performance-complexity trade-off, even after the training phase. Using this model, and a novel adaptive thresholding technique, an algorithm is presented to deliver video encoding within the target coding complexity, while maximizing the coding efficiency. Experimental results justify the superiority of this method over the state-of-the-art methods, with target complexities ranging from 20% to 100%.

近年来，各种视频应用都采用了高效视频编码（HEVC）。由于其对计算的高要求，控制HEVC的复杂性对于满足许多应用中不断变化的需求至关重要，这些应用包括功耗受限的视频编码，视频流和云游戏。大多数现有的复杂度控制方法仅能够考虑决策空间的子集，这导致较低的编码效率。尽管可以将诸如支持向量机（SVM）之类的机器学习方法的效率用于更高精度的决策制定，但当前基于HEVM的基于SVM的技术提供了一个固定的决策边界，这导致了针对不同视频内容的不同编码复杂性。尽管这可能适合降低复杂性，对于复杂性控制，这是不可接受的。本文提出了一种用于编码单元（CU）分区的可调分类方法，解决了所提到的复杂性控制问题。首先，使用图像处理技术设计了一套用于快速CU分区的新颖功能。然后，提出了一种基于支持向量机的灵活分类方法，对CU划分问题进行建模。即使在训练阶段之后，这种方法也可以调整性能复杂度的权衡。使用此模型和新颖的自适应阈值技术，提出了一种算法，可在目标编码复杂度内提供视频编码，同时使编码效率最大化。实验结果证明了该方法优于最新方法的优越性，目标复杂度范围为20％至100％。