Modeling the Perceptual Quality of Immersive Images Rendered on Head Mounted Displays: Resolution and Compression

Huang, Mingkai; Shen, Qing; Ma, Zhan; Bovik, Alan C.; Gupta, P. K. Sen; Zhou, Rongbing; Cao, Xun

doi:10.1109/tip.2018.2865089

Cited by 54 publications

(37 citation statements)

References 23 publications

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“…The use of a second database is very important for model validation and generalization analysis. We tried to use two different databases [19,20] to perform a cross-database validation. Unfortunately, we discovered some inconsistencies in terms of subjective scores that could not be explained.…”

Section: Data and Implementationmentioning

confidence: 99%

Perceptually-Weighted Cnn For 360-Degree Image Quality Assessment Using Visual Scan-Path And Jnd

Sendjasni¹,

Larabi²,

Cheikh³

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Image quality assessment of immersive content and more specifically 360-degree one is still in its infancy. There are many challenges regarding sphere vs. projected representation, human visual system (HVS) properties in a 360-degree environment, etc. In this paper, we propose the use of CNNs to design a no reference model to predict visual quality of 360-degree images. Instead of feeding the CNN with ERPs, visually important viewports are extracted based on visual scan-path prediction and given to a multi-channel CNN using DenseNet-121. Moreover, information about visual fixations and just noticeable difference are used to account for the HVS properties and make the network closer to human judgment. The scan-path is also used to create multiple instances of the database so as to perform a robust generalization analysis and compensate for the lack of databases.

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Section: Data and Implementationmentioning

confidence: 99%

Perceptually-Weighted Cnn For 360-Degree Image Quality Assessment Using Visual Scan-Path And Jnd

Sendjasni¹,

Larabi²,

Cheikh³

2021

2021 IEEE International Conference on Image Processing (ICIP)

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“…Datasets, such as Abreu et al [DA17], Sitzman et al [Sit18], Gutierrez et al [Gut18], were presented to study how the users visualize 360 • images by tracking their attention, head movement and eye movement. For 360 • image visual quality assessment, the most popular dataset are Huang et al [Hua18], CVIQD [Sun17], CVIQD2018 [Sun18] and OIQA [Dua18]. SUN360 [oTed] and 3D60 [oITed] (which is a mix of the SunCG [Soned] and SceneNet [McCed] datasets) and 360SP [Cha18] are interesting benchmarks too.…”

Section: Related Workmentioning

confidence: 99%

An Analysis on Pixel Redundancy Structure in Equirectangular Images

Vazquez¹,

Cutchin²

2021

CSRN

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360◦photogrammetry captures the surrounding light from a central point. To process and transmit these types ofimages over the network to the end user, the most common approach is to project them onto a 2D image usingthe equirectangular projection to generate a 360◦image. However, this projection introduces redundancy into theimage, increasing storage and transmission requirements. To address this problem, the standard approach is touse compression algorithms, such as JPEG or PNG, but they do not take full advantage of the visual redundancyproduced by the equirectangular projection. In this study of the 360SP dataset (a collection of Google StreetView images), we analyze the redundancy in equirectangular images and show how it is structured across theimage. Outcomes from our study will support the developing of spherical compression algorithms, improvingthe immersive experience of Virtual Reality users by reducing loading times and increasing the perceptual imagequality.

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“…After each stimulus, each participant verbally gave a rating score with the grade scale from 1 (bad) to 5 (excellent) which was recorded by an assistant. Similar to [18], the viewing duration of each stimulus was 20 seconds for rating and 5 seconds for a break. To avoid the negative impacts of fatigue and boredom, each participant rated only 36 among 72 stimuli with the total rating duration of approximately 15 minutes.…”

Section: Experiments Settingsmentioning

confidence: 99%

A Weighted Viewport Quality Metric for Omnidirectional Images

Tran

Hoang

Minh

et al. 2020

IEICE Trans. Inf. & Syst.

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Thanks to the ability to bring immersive experiences to users, Virtual Reality (VR) technologies have been gaining popularity in recent years. A key component in VR systems is omnidirectional content, which can provide 360-degree views of scenes. However, at a given time, only a portion of the full omnidirectional content, called viewport, is displayed corresponding to the user's current viewing direction. In this work, we first develop Weighted-Viewport PSNR (W-VPSNR), an objective quality metric for quality assessment of omnidirectional content. The proposed metric takes into account the foveation feature of the human visual system. Then, we build a subjective database consisting of 72 stimuli with spatial varying viewport quality. By using this database, an evaluation of the proposed metric and four conventional metrics is conducted. Experiment results show that the W-VPSNR metric well correlates with the mean opinion scores (MOS) and outperforms the conventional metrics. Also, it is found that the conventional metrics do not perform well for omnidirectional content.

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Modeling the Perceptual Quality of Immersive Images Rendered on Head Mounted Displays: Resolution and Compression

Cited by 54 publications

References 23 publications

Perceptually-Weighted Cnn For 360-Degree Image Quality Assessment Using Visual Scan-Path And Jnd

Perceptually-Weighted Cnn For 360-Degree Image Quality Assessment Using Visual Scan-Path And Jnd

An Analysis on Pixel Redundancy Structure in Equirectangular Images

A Weighted Viewport Quality Metric for Omnidirectional Images

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