How to effectively and accurately measure the degradation of media content is an important research topic in the field of image or video processing. Application scenarios such as online meetings, distance learning, and live game streaming make screen content video become a hot spot in Video Quality Assessment (VQA) research. However, to the best of our knowledge, there is currently no no-reference VQA model designed specifically for screen content videos. In this paper, we propose a blind VQA model for screen content videos. This model first uses a multi-scale approach to extract several groups of features, including gradient features, relative standard deviation features, compression features, frequency domain features and inter-frame features. Through training with labeled videos, the model then uses support vector regressor to map the frame feature vectors to video quality scores. We validate the model on the CSCVQ database. Experiments show that our proposed model outperforms the existing full- and no-reference quality evaluation metrics and is also competitive in terms of stability and computational efficiency.

如何有效准确地衡量媒体内容的退化是图像或视频处理领域的一个重要研究课题。在线会议、远程学习、游戏直播等应用场景使得屏幕内容视频成为视频质量评估（VQA）研究的热点。但是，据我们所知，目前还没有专门为屏幕内容视频设计的无参考 VQA 模型。在本文中，我们提出了一种用于屏幕内容视频的盲 VQA 模型。该模型首先采用多尺度方法提取多组特征，包括梯度特征、相对标准差特征、压缩特征、频域特征和帧间特征。通过带标签的视频训练，然后该模型使用支持向量回归器将帧特征向量映射到视频质量分数。我们在 CSCVQ 数据库上验证模型。实验表明，我们提出的模型优于现有的全参考和无参考质量评估指标，并且在稳定性和计算效率方面也具有竞争力。