Assessing Visual Quality of Omnidirectional Videos

Xu, Mai; Li, Chen; Chen, Zhenzhong; Wang, Zulin; Guan, Zhenyu

doi:10.1109/tcsvt.2018.2886277

Cited by 151 publications

(102 citation statements)

References 46 publications

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“…Planar metrics. None of the metrics proposed in literature [5][6][7][8][9][10][11] addresses the detection of localized distortions in 360-degree content due to lossy compression in CM domain. Therefore, the performance of the proposed method is compared to that of a set of classical FR quality metrics for 2D images, i.e., planar metrics.…”

Section: Methodsmentioning

confidence: 99%

“…Although approaches to quantify the overall visual quality of 360-degree images and videos have been proposed in literature [5][6][7][8][9][10][11], they consider uniformly distributed distortions due to the processing of content in ER domain, rather than localized distortions that might occur when processing the signal in other domains, such as the CM one [2].…”

Section: Introductionmentioning

confidence: 99%

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Graph-Based Detection of Seams In 360-Degree Images

Simone

Azevedo

Kim

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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In this paper, we propose an algorithm to detect a specific kind of distortions, referred to as seams, which commonly occur when a 360-degree image is represented in planar domain by projecting the sphere to a polyhedron, e.g, via the Cube Map (CM) projection, and undergoes lossy compression. The proposed algorithm exploits a graph-based representation to account for the actual sampling density of the 360-degree signal in the native spherical domain. The CM image is considered as a signal lying on a graph defined on the spherical surface. The spectra of the processed and the original signals, computed by applying the Graph Fourier Transform, are compared to detect the seams. To test our method a dataset of compressed CM 360-degree images, annotated by experts, has been created. The performance of the proposed algorithm is compared to those achieved by baseline metrics, as well as to the same approach based on spectral comparison but ignoring the spherical nature of the signal. The experimental results show that the proposed method has the best performance and can successfully detect up to approximately 90% of visible seams on our dataset.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph-Based Detection of Seams In 360-Degree Images

Simone

Azevedo

Kim

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

show abstract

“…In the literature, there have been some studies on subjective quality assessments of omnidirectional content [13][14][15][16]. In these studies, various distortion types such as compression and Gaussian blur are considered.…”

Section: Introductionmentioning

confidence: 99%

“…In these studies, various distortion types such as compression and Gaussian blur are considered. However, the distortions are distributed uniformly in [13][14][15][16]. The work in [7] is the only previous study on omnidirectional content with non-uniform quality.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Retina-Related Zones on Quality Perception of Omnidirectional Image

et al. 2019

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Virtual Reality (VR), which brings immersive experiences to viewers, has been gaining popularity in recent years. A key feature in VR systems is the use of omnidirectional content, which provides 360-degree views of scenes. In this work, we study the human quality perception of omnidirectional images, focusing on different zones surrounding the foveation point. For that purpose, an extensive subjective experiment is carried out to assess the perceptual quality of omnidirectional images with non-uniform quality. Through experimental results, the impacts of different zones are analyzed. Moreover, nineteen objective quality metrics, including foveal quality metrics, are evaluated using our database. It is quantitatively shown that the zones corresponding to the fovea and parafovea of human eyes are extremely important for quality perception, while the impacts of the other zones corresponding to the perifovea and periphery are small. Besides, the investigated metrics are found to be not effective enough to reflect the quality perceived by viewers.

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“…They are efficient and easy to be integrated into codecs but the prediction accuracy is far from satisfactory. Then, some perception-driven IQA metrics for 360 contents were designed via machine learning [22], [23] and deep learning [24], [25]. Chen et al further incorporated SIQA and OIQA and developed a predictive coding based model for 3D 360 image quality assessment [26].…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports

Luo

Zhou

et al. 2019

2019 Picture Coding Symposium (PCS)

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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 multiviewport based full-reference stereo 360 IQA model. Due to the freely changeable viewports when browsing in the head-mounted display, 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.Index Terms-stereoscopic omnidirectional image, multiviewport, image quality assessment, quality of experience

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Assessing Visual Quality of Omnidirectional Videos

Cited by 151 publications

References 46 publications

Graph-Based Detection of Seams In 360-Degree Images

Graph-Based Detection of Seams In 360-Degree Images

Impacts of Retina-Related Zones on Quality Perception of Omnidirectional Image

Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports

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