Signal, Image and Video Processing https://doi.org/10.1007/s11760-022-02372-3 ORIGINAL PAPER A Gabor feature-based full reference video quality assessment model based on spatiotemporal slice of videos Daniel Oppong Bediako 1 · Xuanqin Mou 1 · Maxwell Suobogbiree 2 Received: 2 January 2022 / Revised: 1 September 2022 / Accepted: 19 September 2022 © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract The distortion of video is under the twin impacts of spatial and temporal distortions. Some of the video quality assessment (VQA) metrics assess the video using a combination of these two measures. The accuracy of VQA may be reduced by separating spatial and temporal aspects. The goal of this study was to assess the spatiotemporal structure of video quality by using several VQA databases: (1) The 2D Gabor filter was designed to extract features from both spatial and temporal data, respectively, from video sequences. Moreover, the real and imaginary parts of the 2D Gabor response taken from the reference video and distortion video were then compared with the degree of similarity of the local frame quality. (2) To further enhance the quality of the assessment, we combined the spatial similarity with that of the spatiotemporal similarity and proposed the VQA model, named spatiotemporal slice Gabor feature similarity deviation. (3) In video quality scoring, a sequential pooling strategy was used to assemble the quality indices of frames. (4) Experimental evaluations of the video quality database show that the proposed metric has good consistency with subjective perception and competitive with state-of-the-art full reference video quality assessment models. Keywords Spatiotemporal slice Gabor feature similarity deviation (STS-GFSD) · Full reference video quality assessment (FR-VQA) methods · 2D Gabor filter 1 Introduction Millions of videos are uploaded to social networks and com- munication devices each day. Video algorithms must assess the perceptual quality of videos in a manner that is consis- tent with human judgment. On a frame-by-frame basis, VQA algorithms may produce direct augments of image qual- ity assessment (IQA) approaches, but they frequently result in poor performance. During the purchase, transmission, compression, and reproduction processes, video experiences various distortions, and the problem of video quality turns into a focal concern. There are two ways to measure video quality. The first is through subjective evaluation, which takes many subjects B Daniel Oppong Bediako Xuanqin Mou xqmou@mail.xjtu.edu 1 Institute of Image Processing and Pattern Recognition, Xi’an Jiaotong University, Xi’an, China 2 School of Advance Technologies, Engineering and Science, ACCRA Institute of Technology, Accra, Ghana to give mean opinion scores (MOS) or difference mean opinion scores (DMOS) for each test video. The other is by using objective VQA algorithms. This type of method is an effective way to evaluate video quality and save time, costs, and labor. The objective method has three categories. The full reference (FR) algorithms depend on measuring the error difference (signal difference) between the distorted and the reference video in such a way to imitate the human visual error sensitivity features. Usually, such approaches provide a reference video that is accessible for comparison. Reduce reference (RR) algorithms are suitable for some limited situations on bandwidth that predict video quality by using partial reference video information. No reference (NR) algorithm assesses the quality without referring to the original high-quality image/video. In this paper, we address the task of FR VQA in which a reference video is available to the VQA algorithm. Most VAQ algorithms are based on a frame-by-frame mea- surement. Wang et al. [1] were the first to extend the use of the structure similarity (SSIM) index to VQA. Subsequently, a three-SSIM algorithm [2] was proposed that further consid- 123 Content courtesy of Springer Nature, terms of use apply. Rights reserved.