Perceptual quality assessment of panoramic stitched contents for immersive applications: a prospective survey
1. Department of Computer Science, Kansas State University, Manhattan, KS 66506,USA
2. Department of Software, Sejong University, Seoul 143-747, Republic of Korea
Abstract
Keywords: Virtual reality ; Augmented reality ; Panoramic image ; Immersive contents ; Stitched image quality assessment ; Deep learning ; Convolutional neural networks
Content


Method | Year | Method Description | Dataset | SROCC | PLCC | RMSE | Method Type | |
---|---|---|---|---|---|---|---|---|
Traditional | Deep Learning | |||||||
Yang et al.[24] | 2017 | Investigating optical flow features and structural characteristics for panoramic image quality assessment | SIQA[24] | - | - | 0.2374 | √ | |
Zhou et al.[25] | 2017 | Focusing on scale invariant features i.e., SIFT and BRIEF descriptor for the quality assessment of stitched images | Not Given | - | - | - | √ | |
Xu et al.[26] |
2017 | Examining the consistency analysis of viewing points of the user towards panoramic videos quality assessment | VQA-ODV[61] | - | - | - | √ | |
Zhang et al.[27] | 2017 | Evaluating the quality of panoramic videos using single stimulus continuous quality scale (SSCQS) and subjective assessment of multimedia panoramic video quality (SAMPVIQ) | Panoramic video dataset[27] | 0.7745 | 0.5859 | 13.6107 | √ | |
Yang et al.[28] | 2018 | Exploiting the spatial difference between the panoramic video frames using 3D CNN towards the quality assessment task | VRQ-TJU[28] | 0.8940 | 0.9008 | 8.1985 | √ | |
Guo et al.[30] | 2018 | Perceptual quality assessment of immersive contents using spatial peripheral vision | SUN360[62] | - | - | - | √ | |
Chen et al.[31] | 2018 | Omnidirectional video quality assessment using structural similarity (SSIM) in the spherical domain | Omnidirectional video quality assessment[31] | 0.8211 | 0.8635 | 0.4428 | √ | |
Zhang et al.[32] | 2018 | Providing a generic database for subjective panoramic video quality assessment | Subjective quality database for panoramic videos[32] | 0.8166 | 0.8058 | - | √ | |
Lim et al.[33] | 2018 | Investigating deep adversarial learning and human perception guider for virtual reality image quality assessment | SUN360[62] | 0.8721 | 0.8522 | 8.8048 | √ | |
Li et al.[34] | 2019 | Exploiting perceptual hash, histogram similarity, sparce reconstruction, global color difference, and size of the blind zone for omnidirectional 360° image quality assessment | CROSS[34] | 0.7370 | 0.7370 | 1.3890 | √ | |
Yu et al.[35] | 2019 | Providing cross-reference omnidirectional images dataset for immersive contents quality assessment | CROSS[34] | - | - | - | √ | |
Li et al.[36] | 2019 | Proposing viewports via saliency-driven CNN architecture towards 360° video quality assessment | VQA-ODV[61] | 0.8962 | 0.8740 | 5.7551 | √ | |
Wu et al.[37] | 2019 | Analyzing the perceptual quality of virtual reality videos using 3D CNN | Panoramic video dataset[27] | 0.9601 | 0.9414 | 1.1265 | √ | |
Kim et al.[38] | 2019 | Examining the perceptual quality of virtual reality omnidirectional images using human perception guider | SUN360[62] | 0.8823 | 0.8877 | 6.3837 | √ | |
Azevedo et al.[29] | 2020 | Estimating quality of 360° videos using multi-metric fusion approach | VQA-ODV[61] | 0.9171 | 0.9257 | 4.9954 | √ | |
Yan et al.[39] |
2020 | Focusing on the quality assessment of stereoscopic stitched images using ghost, color, shape, and disparity distortion analysis | VQA-ODV[61] | - | 0.8253 | - | √ | |
Chen et al.[41] | 2020 | Analyzing the quality of stereoscopic omnidirectional images using predictive coding theory based on human perception | OIQA[63] | 0.9020 | 0.9060 | - | √ | |
CVIQD2018[64] | 0.9000 | 0.9070 | ||||||
Yang et al.[42] | 2020 | Examining the quality of panoramic videos using spherical CNN and non-local properties of the immersive contents | VRQ-TJU[28] | 0.9240 | 0.9390 | - | √ | |
Wang et al.[43] | 2021 | Focusing on the quality assessment of stitched images using bi-directional color matching | ISIQA[51] | 0.3340 | 0.3608 | - | √ | |
CCSID[43] | 0.7071 | 0.7380 | 8.6715 |
Method | Year | Method Description | Dataset | SROCC | PLCC | RMSE | Method Type | |
---|---|---|---|---|---|---|---|---|
Traditional | Deep Learning | |||||||
Leorin et al.[44] |
2005 | Focusing on the panoramic video quality assessment using low-level and high-level vision factors | Not Given | - | - | - | √ | |
Xu et al. [45] |
2010 | Evaluating video stitching approaches using color correction in multi-view frame data | Not Given | - | - | - | √ | |
Yang et al.[46] |
2017 | Investigating the perceptual quality of panoramic images using error-activation-guided metric | SIQA[24] | - | - | - | √ | |
Ling et al.[47] |
2018 | Estimating the visual quality of stitching image using trained sparce convolutional kernels and features selection | SIQA[24] | 0.7295 | 0.8574 | 0.3161 | √ | |
Gandhe et al.[48] |
2019 | Investigating the quality of stitched images using hybrid warping approach | Not Given | - | - | - | √ | |
Xia et al.[49] |
2019 | Following the asymmetric processing pipeline of human brain towards panoramic image quality assessment | OIQA[63] | 0.7150 | 0.7408 | 1.4264 | √ | |
Yu et al.[50] |
2019 | Presenting no-reference quality assessment of stitched images using structural properties and saliency features. | Not Given | - | - | - | √ | |
Madhusudana et al.[51] |
2019 | Examining the quality of Virtual reality stitched contents using color correction and bandpass analysis | ISIQA[51] | 0.7820 | 0.8030 | - | √ | |
Li et al.[52] | 2019 | Investigating the quality of 360° omnidirectional contents using deep low-resolution deformation and high-level recurrence | CROSS[34] | 0.7420 | 0.7420 | 2.0670 | √ | |
Sun et al. [55] |
2019 | Evaluating the quality of 360° images using multi-channel CNN architecture | CVIQ[55] | 0.9187 | 0.9247 | 4.6247 | √ | |
Hou et al.[53] |
2020 | Multi-task learning for blind panoramic contents quality assessment | ISIQA[51] | 0.7593 | 0.8022 | - | √ | |
Ullah et al.[54] |
2020 | Analyzing the quality of stitched image using stitching-specific distortion segmentation | SUN360[62] | 0.8591 | 0.9367 | 0.2194 | √ | |
Zheng et al.[40] |
2020 | Focusing on the quality evaluation of omnidirectional images by segmenting the distortion specific regions and spherical projection analysis | CVIQD2018[64] | 0.8614 | 0.9077 | 6.1178 | √ | |
Xu et al.[56] |
2020 | Presenting graph convolutional neural network for viewport prediction towards omnidirectional image quality assessment | OIQA[63] | 0.9050 | 0.9241 | 5.4616 | √ | |
CVIQD2018[64] | 0.7832 | 0.7911 | 1.2934 | |||||
Poreddy et al.[57] | 2021 | Investigating the quality of panoramic contents using scene statistics and univariate generalized gaussian distribution |
LIVE 3D VR IQA[65] |
- | - | - | √ | |
Ding et al.[58] |
2021 | Utilizing the adjacent pixels correlation technique towards the quality assessment of panoramic image | OIQA[63] | 0.9394 | 0.9466 | 0.7142 | √ | |
CVIQD2018[64] | 0.9322 | 0.9496 | 4.3690 | |||||
Zhou et al.[59] |
2021 | Exploiting local-global naturalness and multifrequency analysis for 360° image quality evaluation | OIQA[63] | 0.9614 | 0.9695 | 0.5146 | √ | |
CVIQD2018[64] | 0.9670 | 0.9751 | 3.1036 | |||||
Zhang et al.[60] |
2021 | Utilizing spatial domain features extraction and temporal pooling for panoramic video quality assessment | Subjective quality database for panoramic videos[32] | 0.7754 | 0.8121 | 0.4499 | √ | |
Sendjasni et al.[66] | 2021 | Exploiting perceptually-weight CNN architecture followed by visual scan-path for the quality assessment of 360° images | CVIQD2018[64] | 0.9280 | 0.9490 | - | √ | |
Tian et al.[67] |
2021 | Investigating global statistical characteristics and local measurement errors for stitched image quality evaluation | ISIQA[51] | 0.8406 | 0.8532 | 6.7551 | √ | |
CCSID[43] | 0.7632 | 0.7776 | 8.3911 | |||||
Zhou et al.[68] |
2021 | Proposing multi-stream CNN network with distortion discrimination for omnidirectional contents quality assessment | OIQA[63] | 0.9230 | 0.8990 | 6.3960 | √ | |
CVIQD2018[64] | 0.9280 | 0.9490 | - |


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