Abstract
A general-purpose no-reference video quality assessment algorithm based on a long short-term memory (LSTM) network and a pretrained convolutional neural network (CNN) is introduced. Considering video sequences as a time series of deep features extracted with the help of a CNN, an LSTM network is trained to predict subjective quality scores. In contrast to previous methods, the resulting algorithm was trained on the recently published Konstanz Natural Video Quality Database (KoNViD-1k), which is the only publicly available database that contains sequences with authentic distortions. The results of experiments on KoNViD-1k demonstrate that the proposed method outperforms other state-of-the-art algorithms. Furthermore, these results are also confirmed using tests on the LIVE Video Quality Assessment Database, which consists of artificially distorted videos.
Highlights
In recent years, we have witnessed an explosive growth in the spread of multimedia technologies and digital visual content
In our proposed NRVQA framework, we model a digital video sequence as a sequence of data of frame-level deep features extracted via pretrained convolutional neural network (CNN)
Unlike other long short-term memory (LSTM) applying NR-video quality assessment (VQA) methods [3,21], we model video sequences as sequential data of frame-level deep features and not employing image quality-related metrics at all
Summary
We have witnessed an explosive growth in the spread of multimedia technologies and digital visual content. With the increasing popularity of smart phones, social media, and video-sharing applications, digital videos are increasingly captured, transmitted, stored, shared, compressed, or edited. These transformations usually affect the perceived visual quality of videos. Humans are the end consumers of digital video content whose quality requirements have to be satisfied. This has motivated video service providers and the research community to devise quality assessment methods for digital videos. Huge amounts of research have been conducted to reveal the psychological and physiological mechanisms of the HVS, The goal of objective VQA is to design mathematical models that are able to predict the quality of a video assessed by humans. According to the availability of reference videos, VQA methods can be divided into three groups: full-reference (FR-VQA), reduced-reference (RRVQA), and no-reference (NR-VQA) algorithms
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