7 research outputs found
Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports
Objective quality assessment of stereoscopic panoramic images becomes a
challenging problem owing to the rapid growth of 360-degree contents. Different
from traditional 2D image quality assessment (IQA), more complex aspects are
involved in 3D omnidirectional IQA, especially unlimited field of view (FoV)
and extra depth perception, which brings difficulty to evaluate the quality of
experience (QoE) of 3D omnidirectional images. In this paper, we propose a
multi-viewport based fullreference stereo 360 IQA model. Due to the freely
changeable viewports when browsing in the head-mounted display (HMD), our
proposed approach processes the image inside FoV rather than the projected one
such as equirectangular projection (ERP). In addition, since overall QoE
depends on both image quality and depth perception, we utilize the features
estimated by the difference map between left and right views which can reflect
disparity. The depth perception features along with binocular image qualities
are employed to further predict the overall QoE of 3D 360 images. The
experimental results on our public Stereoscopic OmnidirectionaL Image quality
assessment Database (SOLID) show that the proposed method achieves a
significant improvement over some well-known IQA metrics and can accurately
reflect the overall QoE of perceived images
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
Stereoscopic Omnidirectional Image Quality Assessment Based on Predictive Coding Theory
Objective quality assessment of stereoscopic omnidirectional images is a
challenging problem since it is influenced by multiple aspects such as
projection deformation, field of view (FoV) range, binocular vision, visual
comfort, etc. Existing studies show that classic 2D or 3D image quality
assessment (IQA) metrics are not able to perform well for stereoscopic
omnidirectional images. However, very few research works have focused on
evaluating the perceptual visual quality of omnidirectional images, especially
for stereoscopic omnidirectional images. In this paper, based on the predictive
coding theory of the human vision system (HVS), we propose a stereoscopic
omnidirectional image quality evaluator (SOIQE) to cope with the
characteristics of 3D 360-degree images. Two modules are involved in SOIQE:
predictive coding theory based binocular rivalry module and multi-view fusion
module. In the binocular rivalry module, we introduce predictive coding theory
to simulate the competition between high-level patterns and calculate the
similarity and rivalry dominance to obtain the quality scores of viewport
images. Moreover, we develop the multi-view fusion module to aggregate the
quality scores of viewport images with the help of both content weight and
location weight. The proposed SOIQE is a parametric model without necessary of
regression learning, which ensures its interpretability and generalization
performance. Experimental results on our published stereoscopic omnidirectional
image quality assessment database (SOLID) demonstrate that our proposed SOIQE
method outperforms state-of-the-art metrics. Furthermore, we also verify the
effectiveness of each proposed module on both public stereoscopic image
datasets and panoramic image datasets
Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networks
Quality assessment of omnidirectional images has become increasingly urgent
due to the rapid growth of virtual reality applications. Different from
traditional 2D images and videos, omnidirectional contents can provide
consumers with freely changeable viewports and a larger field of view covering
the spherical surface, which makes the objective
quality assessment of omnidirectional images more challenging. In this paper,
motivated by the characteristics of the human vision system (HVS) and the
viewing process of omnidirectional contents, we propose a novel Viewport
oriented Graph Convolution Network (VGCN) for blind omnidirectional image
quality assessment (IQA). Generally, observers tend to give the subjective
rating of a 360-degree image after passing and aggregating different viewports
information when browsing the spherical scenery. Therefore, in order to model
the mutual dependency of viewports in the omnidirectional image, we build a
spatial viewport graph. Specifically, the graph nodes are first defined with
selected viewports with higher probabilities to be seen, which is inspired by
the HVS that human beings are more sensitive to structural information. Then,
these nodes are connected by spatial relations to capture interactions among
them. Finally, reasoning on the proposed graph is performed via graph
convolutional networks. Moreover, we simultaneously obtain global quality using
the entire omnidirectional image without viewport sampling to boost the
performance according to the viewing experience. Experimental results
demonstrate that our proposed model outperforms state-of-the-art full-reference
and no-reference IQA metrics on two public omnidirectional IQA databases