To form a high-performance video quality predictor, we developed a framework for full-reference (FR) video quality assessment that integrates Spatio-temporal slice analysis (STS) to create a high-performance predictor of video quality. However, both gradient and Gabor are spatial–temporal structural capturers used for the simultaneous extraction of both spatial and temporal features. In this paper, we proposed a novel VQA algorithm via a joint model of gradient magnitude and Gabor features (JMG) between the STS images of the reference videos and their distorted counterparts to assess the degradation of video quality effectively. Firstly, gradient magnitude and the Gabor filter were constructed to extract the spatiotemporal features of the video sequence. However, the two-feature model combined to predict the perceptual quality of frames. This new proposed VQA model is known as the horizontal and time STS (HT-JMG) model. To further investigate the influence of spatial dissimilarity, we combined the frame-by-frame spatial T-JMG(S) factor with the HT-JMG and propose another VQA model, called the time, horizontal, and vertical STS (THV-JMG) model. Finally, the results of the experiment showed that the proposed method has a strong correlation with subjective perception and is competitive with state-of-the-art full reference VQA models.

为了形成高性能视频质量预测器，我们开发了一个完整参考 (FR) 视频质量评估框架，该框架集成了时空切片分析 (STS) 以创建高性能视频质量预测器。然而，梯度和 Gabor 都是用于同时提取空间和时间特征的时空结构捕获器。在本文中，我们通过参考视频的 STS 图像与其失真对应物之间的梯度幅度和 Gabor 特征 (JMG) 的联合模型提出了一种新的 VQA 算法，以有效评估视频质量的下降。首先，构造梯度幅度和Gabor滤波器来提取视频序列的时空特征。然而，结合两个特征模型来预测帧的感知质量。这种新提出的 VQA 模型被称为水平和时间 STS (HT-JMG) 模型。为了进一步研究空间​​差异的影响，我们将逐帧空间 T-JMG(S) 因子与 HT-JMG 相结合，并提出了另一个 VQA 模型，称为时间、水平和垂直 STS (THV-JMG)模型。最后，实验结果表明，所提出的方法与主观感知具有很强的相关性，并且与最先进的全参考 VQA 模型具有竞争力。