The Screen Content Coding Extension in High-Efficiency Video Coding standard (HEVC-SCC) promotes the capabilities of HEVC in coding screen content videos (SCVs) using new techniques, which improves coding efficiency dramatically. These new techniques depend on the distinguished features of SCV such as repeated patterns, limited number of colors, sharp edges, and non-noisy regions. Nonetheless, this coding efficiency comes at the cost of enormous computational complexity. In this paper, a new technique is proposed to save encoding time while conserving coding efficiency. The proposed algorithm selects the suitable mode for each Coding Unit (CU) and skips unhelpful modes by two methods. Two methods depend on skipping unwanted modes by Neural Network Classifiers. The first classifier is Neural Network Classifier Based on Current Depth Features (NNC_CF), which depends on the CU current depth features. The second one is Neural Network Classifier Based on Parent Depth Features (NNC_PF); the Parent depth features are considered the input of this classifier. The simulation results demonstrate the efficacy of the proposed scheme.

高效视频编码标准中的屏幕内容编码扩展 (HEVC-SCC) 使用新技术提升了 HEVC 在编码屏幕内容视频 (SCV) 方面的能力，从而显着提高了编码效率。这些新技术取决于 SCV 的显着特征，例如重复的图案、有限的颜色、锐利的边缘和无噪声区域。尽管如此，这种编码效率是以巨大的计算复杂性为代价的。在本文中，提出了一种新技术来节省编码时间，同时保持编码效率。所提出的算法为每个编码单元（CU）选择合适的模式并通过两种方法跳过无用的模式。两种方法依赖于通过神经网络分类器跳过不需要的模式。第一个分类器是基于当前深度特征的神经网络分类器（NNC_CF），它依赖于CU当前深度特征。第二个是基于父深度特征的神经网络分类器（NNC_PF）；父深度特征被认为是这个分类器的输入。仿真结果证明了所提出方案的有效性。