Multi-Angle Projection Based Blind Omnidirectional Image Quality Assessment

Jiang, Hao; Jiang, Gangyi; Yu, Mei; Luo, Ting; Xu, Haiyong

doi:10.1109/tcsvt.2021.3128014

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…Zheng et al 12 believed that the stretch loss caused by EPR format mapping panoramic images would seriously affect the quality of panoramic images; therefore, they used the SSP format to represent panoramic images, but it will cause more calculation of the model. Motivated by the perspective of perception modeling, Jiang et al 30 proposed a multi-angle projection-based BOIQA (MP-BOIQA) model based on the color omnidirectional distortion units. Compared with FR and RR models, the above BOIQA models achieve better performances.…”

Section: Related Workmentioning

confidence: 99%

Multifaceted perception-based blind omnidirectional image quality assessment

Liu,

Wang,

Jiang

et al. 2023

J. Electron. Imag.

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With the rapid development of computer vision, the omnidirectional image (OI) with a broader view and a higher resolution provides humans with an immersive visual experience and has attracted more researchers' attention. But current image quality assessment models cannot perform well due to neglecting the difference between panoramic images and traditional images, limiting OIs' further development. Inspired by the above problem, we propose a blind OI quality assessment model based on multi-frequency features, color features, and human visual system-based saliency features. Specifically, we utilize the high-and low-frequency information mapped to the frequency domain to measure the frequency domain loss. The local binary pattern operator is applied to encode the different color channel information. Then the local natural scene statistics and entropy features are extracted using the input image to weight the obtained saliency image by our model to reduce the model's computational complexity. Finally, support vector regression is employed to predict the OIs' quality scores. Experimental results on CVIQD and OIQA databases prove that our work performs better than state-of-the-art OIQA models.

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Section: Related Workmentioning

confidence: 99%

Multifaceted perception-based blind omnidirectional image quality assessment

Liu,

Wang,

Jiang

et al. 2023

J. Electron. Imag.

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show abstract

“…Zhou et al [9] achieved panoramic image quality assessment score by analyzing multifrequency information and statistically evaluating the local and global naturalness presented in both ERP and VP formats. Jiang et al [8] explored the color information of each VP image unit in the rotated Cubemap Projection (CMP) format through tensor decomposition and piecewise exponential fitting. The above-mentioned works achieved satisfactory performance results by designing hand-crafted features through techniques such as machine learning.…”

Section: A Traditional Oiqa Metricsmentioning

confidence: 99%

“…Therefore, it is crucial to develop blind/no-reference omnidirectional image quality assessment (BOIQA/NR-OIQA) methods that can evaluate the quality of OIs without reference images. Regarding the NR-OIQA type, many approaches [7,8,9,10] commonly involve filtering to analyze the frequency domain information or natural scene statistics (NSS) to find statistical regularities in OIs. However, the manual feature designing is challenging [11], which limits the robustness of those methods.…”

Section: Introductionmentioning

confidence: 99%

Dual-Level Blind Omnidirectional Image Quality Assessment Network Based on Human Visual Perception

Liu,

Zhang,

Wan

et al. 2023

IJACSA

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With the rapid development of virtual reality (VR) technology, a large number of omnidirectional images (OIs) with uncertain quality are flooding into the internet. As a result, Blind Omnidirectional Image Quality Assessment (BOIQA) has become increasingly urgent. The existing solutions mainly focus on manually or automatically extracting high-level features from OIs, which overlook the important guiding role of human visual perception in this immersive experience. To address this issue, a dual-level network based on human visual perception is developed in this paper for BOIQA. Firstly, a human attention branch is proposed, in which the transformer-based model can efficiently represent attentional features of the human eye within a multidistance perception image pyramid of viewport. Then, inspired by the hierarchical perception of human visual system, a multiscale perception branch is designed, in which hierarchical features of six orientational viewports are considered and obtained by a residual network in parallel. Additionally, the correlation features among viewports are investigated to assist the multiviewport feature fusion, in which the feature maps extracted from different viewports are further measured for their similarity and correlation by the attention-based module. Finally, the output values from both branches are regressed by fully connected layer to derive the final predicted quality score. Comprehensive experiments on two public datasets demonstrate the significant superiority of the proposed method.

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MTIQA360: An Easily Trainable Multitasking Network for Blind Omnidirectional Image Quality Assessment

Wang,

Liu

2024

Lecture Notes in Computer Science

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Multi-Angle Projection Based Blind Omnidirectional Image Quality Assessment

Cited by 12 publications

References 32 publications

Multifaceted perception-based blind omnidirectional image quality assessment

Multifaceted perception-based blind omnidirectional image quality assessment

Dual-Level Blind Omnidirectional Image Quality Assessment Network Based on Human Visual Perception

MTIQA360: An Easily Trainable Multitasking Network for Blind Omnidirectional Image Quality Assessment

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