Ultra-high definition video quality assessment method based on deep learning

With the development of display technology, people’s demand for high-definition video is increasing, and the requirements for video quality are also increasing. Nowadays, a large number of videos have emerged, and how to distinguish the quality of videos faces many challenges. Nowadays, deep learning is developing rapidly, and many video quality assessment (VQA) methods have emerged. These VQA methods can efficiently evaluate video quality and save a lot of manpower and material resources. To achieve the evaluation of UHD video quality, we propose an inter-frame downsampling UHD video quality assessment model (IFDU-VQA) based on deep learning. This model is a no-reference (NR) evaluation model that adopts a dual-branch structure: one branch uses a recursive network to deeply extract video inter-frame features, and combines inter-frame downsampling technology with a multi-information fusion (MIF) module to achieve different time and frequency information. Effective fusion; the other branch applies convolutional neural networks (CNN) to extract features from individual video frames. By fusing the outputs of the two branches, regression and pooling modules are used to calculate the final quality score of the video. Compared with multiple existing models, experimental results show that IFDU-VQA demonstrates superior performance on multiple standard data sets.