656 research outputs found
Visual Comfort Assessment for Stereoscopic Image Retargeting
In recent years, visual comfort assessment (VCA) for 3D/stereoscopic content
has aroused extensive attention. However, much less work has been done on the
perceptual evaluation of stereoscopic image retargeting. In this paper, we
first build a Stereoscopic Image Retargeting Database (SIRD), which contains
source images and retargeted images produced by four typical stereoscopic
retargeting methods. Then, the subjective experiment is conducted to assess
four aspects of visual distortion, i.e. visual comfort, image quality, depth
quality and the overall quality. Furthermore, we propose a Visual Comfort
Assessment metric for Stereoscopic Image Retargeting (VCA-SIR). Based on the
characteristics of stereoscopic retargeted images, the proposed model
introduces novel features like disparity range, boundary disparity as well as
disparity intensity distribution into the assessment model. Experimental
results demonstrate that VCA-SIR can achieve high consistency with subjective
perception
Ordering graphs with small index and its application
AbstractWe consider the problem of ordering connected graphs by index (the largest eigenvalue). The asymptotic ordering for the connected graphs with index less than 2+5 is determined. Its application to the study of acyclic Kekulean molecules with big HOMO–LUMO separation is also given
Deep Local and Global Spatiotemporal Feature Aggregation for Blind Video Quality Assessment
In recent years, deep learning has achieved promising success for multimedia
quality assessment, especially for image quality assessment (IQA). However,
since there exist more complex temporal characteristics in videos, very little
work has been done on video quality assessment (VQA) by exploiting powerful
deep convolutional neural networks (DCNNs). In this paper, we propose an
efficient VQA method named Deep SpatioTemporal video Quality assessor (DeepSTQ)
to predict the perceptual quality of various distorted videos in a no-reference
manner. In the proposed DeepSTQ, we first extract local and global
spatiotemporal features by pre-trained deep learning models without fine-tuning
or training from scratch. The composited features consider distorted video
frames as well as frame difference maps from both global and local views. Then,
the feature aggregation is conducted by the regression model to predict the
perceptual video quality. Finally, experimental results demonstrate that our
proposed DeepSTQ outperforms state-of-the-art quality assessment algorithms
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