2,408 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
No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness
360-degree/omnidirectional images (OIs) have achieved remarkable attentions
due to the increasing applications of virtual reality (VR). Compared to
conventional 2D images, OIs can provide more immersive experience to consumers,
benefitting from the higher resolution and plentiful field of views (FoVs).
Moreover, observing OIs is usually in the head mounted display (HMD) without
references. Therefore, an efficient blind quality assessment method, which is
specifically designed for 360-degree images, is urgently desired. In this
paper, motivated by the characteristics of the human visual system (HVS) and
the viewing process of VR visual contents, we propose a novel and effective
no-reference omnidirectional image quality assessment (NR OIQA) algorithm by
Multi-Frequency Information and Local-Global Naturalness (MFILGN).
Specifically, inspired by the frequency-dependent property of visual cortex, we
first decompose the projected equirectangular projection (ERP) maps into
wavelet subbands. Then, the entropy intensities of low and high frequency
subbands are exploited to measure the multi-frequency information of OIs.
Besides, except for considering the global naturalness of ERP maps, owing to
the browsed FoVs, we extract the natural scene statistics features from each
viewport image as the measure of local naturalness. With the proposed
multi-frequency information measurement and local-global naturalness
measurement, we utilize support vector regression as the final image quality
regressor to train the quality evaluation model from visual quality-related
features to human ratings. To our knowledge, the proposed model is the first
no-reference quality assessment method for 360-degreee images that combines
multi-frequency information and image naturalness. Experimental results on two
publicly available OIQA databases demonstrate that our proposed MFILGN
outperforms state-of-the-art approaches
Blind assessment for stereo images considering binocular characteristics and deep perception map based on deep belief network
© 2018 Elsevier Inc. In recent years, blind image quality assessment in the field of 2D image/video has gained the popularity, but its applications in 3D image/video are to be generalized. In this paper, we propose an effective blind metric evaluating stereo images via deep belief network (DBN). This method is based on wavelet transform with both 2D features from monocular images respectively as image content description and 3D features from a novel depth perception map (DPM) as depth perception description. In particular, the DPM is introduced to quantify longitudinal depth information to align with human stereo visual perception. More specifically, the 2D features are local histogram of oriented gradient (HoG) features from high frequency wavelet coefficients and global statistical features including magnitude, variance and entropy. Meanwhile, the global statistical features from the DPM are characterized as 3D features. Subsequently, considering binocular characteristics, an effective binocular weight model based on multiscale energy estimation of the left and right images is adopted to obtain the content quality. In the training and testing stages, three DBN models for the three types features separately are used to get the final score. Experimental results demonstrate that the proposed stereo image quality evaluation model has high superiority over existing methods and achieve higher consistency with subjective quality assessments
Full-reference stereoscopic video quality assessment using a motion sensitive HVS model
Stereoscopic video quality assessment has become a major research topic in recent years. Existing stereoscopic video quality metrics are predominantly based on stereoscopic image quality metrics extended to the time domain via for example temporal pooling. These approaches do not explicitly consider the motion sensitivity of the Human Visual System (HVS). To address this limitation, this paper introduces a novel HVS model inspired by physiological findings characterising the motion sensitive response of complex cells in the primary visual cortex (V1 area). The proposed HVS model generalises previous HVS models, which characterised the behaviour of simple and complex cells but ignored motion sensitivity, by estimating optical flow to measure scene velocity at different scales and orientations. The local motion characteristics (direction and amplitude) are used to modulate the output of complex cells. The model is applied to develop a new type of full-reference stereoscopic video quality metrics which uniquely combine non-motion sensitive and motion sensitive energy terms to mimic the response of the HVS. A tailored two-stage multi-variate stepwise regression algorithm is introduced to determine the optimal contribution of each energy term. The two proposed stereoscopic video quality metrics are evaluated on three stereoscopic video datasets. Results indicate that they achieve average correlations with subjective scores of 0.9257 (PLCC), 0.9338 and 0.9120 (SRCC), 0.8622 and 0.8306 (KRCC), and outperform previous stereoscopic video quality metrics including other recent HVS-based metrics
Methods for reducing visual discomfort in stereoscopic 3D: A review
This work was supported by the EPSRC Grant EP/M01469X/1, “Geometric Evaluation of Stereoscopic Video”
Stereoscopic video quality assessment using binocular energy
Stereoscopic imaging is becoming increasingly popular. However, to ensure the best quality of experience, there is a need to develop more robust and accurate objective metrics for stereoscopic content quality assessment. Existing stereoscopic image and video metrics are either extensions of conventional 2D
metrics (with added depth or disparity information) or are based on relatively simple perceptual models. Consequently, they tend to lack the accuracy and robustness required for stereoscopic content quality assessment. This paper introduces full-reference stereoscopic image and video quality metrics based on a Human
Visual System (HVS) model incorporating important physiological findings on binocular vision. The proposed approach is based on the following three contributions. First, it introduces a novel HVS model extending previous models to include the phenomena of binocular suppression and recurrent excitation. Second, an image quality metric based on the novel HVS model
is proposed. Finally, an optimised temporal pooling strategy is introduced to extend the metric to the video domain. Both image and video quality metrics are obtained via a training procedure to establish a relationship between subjective scores and objective measures of the HVS model. The metrics are evaluated using
publicly available stereoscopic image/video databases as well as a new stereoscopic video database. An extensive experimental evaluation demonstrates the robustness of the proposed quality metrics. This indicates a considerable improvement with respect to the state-of-the-art with average correlations with subjective
scores of 0.86 for the proposed stereoscopic image metric and 0.89 and 0.91 for the proposed stereoscopic video metrics
Saliency detection for stereoscopic images
International audienceSaliency detection techniques have been widely used in various 2D multimedia processing applications. Currently, the emerging applications of stereoscopic display require new saliency detection models for stereoscopic images. Different from saliency detection for 2D images, depth features have to be taken into account in saliency detection for stereoscopic images. In this paper, we propose a new stereoscopic saliency detection framework based on the feature contrast of color, intensity, texture, and depth. Four types of features including color, luminance, texture, and depth are extracted from DC-T coefficients to represent the energy for image patches. A Gaussian model of the spatial distance between image patches is adopted for the consideration of local and global contrast calculation. A new fusion method is designed to combine the feature maps for computing the final saliency map for stereoscopic images. Experimental results on a recent eye tracking database show the superior performance of the proposed method over other existing ones in saliency estimation for 3D images
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