Full-Reference Video Quality Assessment Based on Spatiotemporal Visual Sensitivity

Huiyuan Fu, Da Pan, Ping Shi
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引用次数: 2

Abstract

Video streaming services have become one of the important businesses of network service providers. Accurately predicting video perceptual quality score can help providing high-quality video services. Many video quality assessment (VQA) methods were trying to simulate human visual system (HVS) to get a better performance. In this paper, we proposed a full-reference video quality assessment (FR-VQA) method named DeepVQA-FBSA. Our method is based on spatiotemporal visual sensitivity. It firstly uses a convolutional neural network (CNN) to obtain the visual sensitivity maps of frames according to the input spatiotemporal information. Then visual sensitivity maps are used to obtain the perceptual features of every frame which we called frame-level features in this paper. The frame-level features are then feed into a Feature Based Self-attention (FBSA) module to fusion to the video-level features and used to predict the video quality score. The experimental results showed that the predicted results of our method have great consistency with the subjective evaluation results.
基于时空视觉灵敏度的全参考视频质量评价
视频流服务已成为网络服务提供商的重要业务之一。准确预测视频感知质量评分有助于提供高质量的视频服务。许多视频质量评估方法都试图模拟人类视觉系统以获得更好的效果。本文提出了一种全参考视频质量评估(FR-VQA)方法,命名为DeepVQA-FBSA。我们的方法是基于时空视觉灵敏度。首先利用卷积神经网络(CNN)根据输入的时空信息获取帧的视觉灵敏度图;然后利用视觉灵敏度图获得每一帧的感知特征,本文称之为帧级特征。然后将帧级特征输入到基于特征的自关注(FBSA)模块中,与视频级特征融合,用于预测视频质量分数。实验结果表明,该方法的预测结果与主观评价结果有很大的一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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