Abstract
Video quality assessment (VQA) is an important element of various applications ranging from automatic video streaming to display technology. Furthermore, visual quality measurements require a balanced investigation of visual content and features. Previous studies have shown that the features extracted from a pretrained convolutional neural network are highly effective for a wide range of applications in image processing and computer vision. In this study, we developed a novel architecture for no-reference VQA based on the features obtained from pretrained convolutional neural networks, transfer learning, temporal pooling, and regression. In particular, we obtained solutions by only applying temporally pooled deep features and without using manually derived features. The proposed architecture was trained based on the recently published Konstanz natural video quality database (KoNViD-1k), which contains 1200 video sequences with authentic distortion unlike other publicly available databases. The experimental results obtained based on KoNViD-1k demonstrated that the proposed method performed better than other state-of-the-art algorithms. Furthermore, these results were confirmed by tests using the LIVE VQA database, which contains artificially distorted videos.
Highlights
Multimedia technology and digital visual signal processing have developed rapidly during recent decades
The proposed NR-video quality assessment (VQA) algorithms were evaluated based on their performance with the benchmark VQA databases, which were labeled with the subjective scores and mean opinion score (MOS) values representing the overall image quality
We developed a novel framework for no-reference VQA (NR-VQA) based on the features obtained from pretrained convolutional neural networks (CNNs) (Inception-V3 [32] and Inception-ResNet-V2 [31]), transfer learning, temporal pooling, and regression
Summary
Multimedia technology and digital visual signal processing have developed rapidly during recent decades. Digital images and videos are very easy to create, transmit, store, and share. Owing to these developments, the design of reliable video quality assessment (VQA) algorithms has attracted considerable attention. VQA has been the focus of many research studies and patents. The vast volume of user-created digital video content has led to the development of numerous VQA applications, which require reliable and effective quality monitoring [39].
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