利用基于人类注意力的深度视觉和差异特征评估立体图像的视觉舒适度

Hyunwook Jeong, Hak Gu Kim, Yong Man Ro
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引用次数: 15

摘要

本文提出了一种利用深度学习对立体图像进行视觉舒适度评估(VCA)的新方法。为了预测人类视觉系统在观看立体图像时的视觉不适感,我们设计了 VCA 深度网络来潜编码感知线索,即立体图像之间的视觉差异以及基于人类注意力的差异幅度和梯度信息。为了从左右视图中提取视觉差异特征,我们采用了连体网络。此外,基于人类视觉系统(HVS)的人类注意力区域差距幅值和梯度图被馈送到两个单独的深度卷积神经网络(DCNN),以获得与差距相关的特征。最后,通过汇总这些感知特征,所提出的方法可直接预测最终的视觉舒适度得分。我们在 IEEE-SA 数据集上进行了广泛的对比实验。实验结果表明,与现有方法相比,所提出的方法具有出色的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual comfort assessment of stereoscopic images using deep visual and disparity features based on human attention
This paper proposes a novel visual comfort assessment (VCA) for stereoscopic images using deep learning. To predict visual discomfort of human visual system in stereoscopic viewing, we devise VCA deep networks to latently encode perceptual cues, which are visual differences between stereoscopic images and human attention-based disparity magnitude and gradient information. To extract the visual difference features from left and right views, a Siamese network is employed. In addition, human attention region-based disparity magnitude and gradient maps are fed to two individual deep convolutional neural networks (DCNNs) for disparity-related features based on human visual system (HVS). Finally, by aggregating these perceptual features, the proposed method directly predicts the final visual comfort score. Extensive and comparative experiments have been conducted on IEEE-SA dataset. Experimental results show that the proposed method can yield excellent correlation performance compared to existing methods.
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