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Due to the growing demand for high-quality video services in 4G and 5G applications, measuring the quantitative quality of video services is expected to become a major vital task. The no-reference video quality assessment (NR-VQA) work published so far regresses computationally complex statistical transforms or convolutional neural network (CNN) features to predict a quality score. In this article, we propose a novel NR-VQA scheme using systematic sampling of spatiotemporal planes ( , , and ) based on the high standard deviation ( ) of their high-frequency bands to represent distortion. The human visual system (HVS) is highly sensitive to structural information in visual scenes, and distortions disrupt the structural properties. The proposed scheme encodes two-level, 3-D structural video information using novel local spatiotemporal tetra patterns (LSTP) on the sampled highest planes from each block of …