A novel no-reference (NR) video quality metric (VQM) is proposed in this paper based on two deep neural networks (NN), namely 3D convolution network (3D-CNN) ...

Video quality assessment (VQA) is a challenging task due to the complexity of modeling perceived quality characteristics in both.

Apr 29, 2022 · In this paper, we propose a very simple but effective UGC VQA model, which tries to address this problem by training an end-to-end spatial feature extraction ...

A variety of video quality models apply convolutional neural networks (CNNs or ConvNets) to extract both low-level and high-level semantic features from ...

Jul 12, 2024 · We demonstrate that NR-VMAF outperforms current state-of-the-art NR metrics while achieving a prediction accuracy that is comparable to VMAF and ...

In this paper, we propose a novel deep learning NR-VQA method based on multi-task learning and temporal attention to predict the video quality for both ...

A no-reference video quality assessment method for in-capture distortions using two CNNs for encoding video frames in terms of both semantics and quality ...

Jun 2, 2021 · In this paper, we propose a deep learning based video quality assessment (VQA) framework to evaluate the quality of the compressed user's ...

We propose a 3D deep convolutional neural network (3D CNN) to evaluate video quality without reference by generating spatial/temporal deep features within ...

Jun 30, 2022 · This paper proposes a video quality assessment method (VQA) incorporating a dual-depth network architecture.