{"title":"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","authors":"Hyunwook Jeong, Hak Gu Kim, Yong Man Ro","doi":"10.1109/ICIP.2017.8296374","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Visual comfort assessment of stereoscopic images using deep visual and disparity features based on human attention\",\"authors\":\"Hyunwook Jeong, Hak Gu Kim, Yong Man Ro\",\"doi\":\"10.1109/ICIP.2017.8296374\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":229602,\"journal\":{\"name\":\"2017 IEEE International Conference on Image Processing (ICIP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Conference on Image Processing (ICIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP.2017.8296374\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Image Processing (ICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2017.8296374","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.