Blind Omnidirectional Image Quality Assessment Based on Structure and Natural Features

Liu, Yun; Yu, Hongwei; Huang, Baoqing; Yue, Guanghui; Song, Baoyan

doi:10.1109/tim.2021.3102691

Cited by 21 publications

(7 citation statements)

References 55 publications

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“…Therefore, the FR and RR methods are ignored, and many blind image quality assessment (BIQA) models have been proposed. 27 For traditional BIQA, many methods extract handcrafted distortion-discriminative features for predicting the perceptual image quality, such as the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) 28 and the Natural Image Quality Evaluator (NIQE) measure. 29 Similar to the full reference methods, directly using the NR-IQA method to predict panoramic images cannot achieve good performance because the visual characteristics of panoramic images are not considered by NR-IQA methods.…”

Section: Related Workmentioning

confidence: 99%

“…Although FR and RR metrics can boost their accuracy with the information of the pristine signal, in actual situations, perfect-quality reference images are always difficult to acquire. Therefore, the FR and RR methods are ignored, and many blind image quality assessment (BIQA) models have been proposed 27 . For traditional BIQA, many methods extract handcrafted distortion-discriminative features for predicting the perceptual image quality, such as the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) 28 and the Natural Image Quality Evaluator (NIQE) measure 29 .…”

Section: Related Workmentioning

confidence: 99%

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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%

Section: Related Workmentioning

confidence: 99%

Multifaceted perception-based blind omnidirectional image quality assessment

Liu,

Wang,

Jiang

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…The edges in an image can convey crucial visual information. The gradient is a widely utilized method in the domain of IQA 29 – 33 due to its capability of detecting variations in structure and contrast. Gradient magnitude (GM) map is generally calculated using the gradient operator to convolve the input image and obtain its horizontal and vertical components, and then calculating the GM map based on them.…”

Section: Proposed Mcvsimentioning

confidence: 99%

Visual security index of selective encrypted images based on multi-directional structure and content-aware features

et al. 2023

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Selective encryption algorithms have emerged as a popular technique for protecting the privacy of images during real-time transmission. For selectively encrypted images, it is necessary to evaluate their security and usability with visual security indices, and there have been a series of studies in this area. However, those proposed visual security indices (VSI) are often ineffective. We present a multidirectional structure and content-aware features-based visual security index (MCVSI) to perform an objective assessment on selectively encrypted images. Considering that selectively encrypted images prevent the main contents from being easily identified, stable local features in the images are extracted to indicate the degree of image content leakage. Meanwhile, we extract spatial structure information that closely aligns with human visual perception to indicate the level of variation in the overall image skeleton. Next, these features are subjected to similarity measurements to produce two types of similarity, content perception feature similarity and structure feature similarity. Finally, our visual security index is built by connecting all feature similarities and their corresponding visual security scores using the regression module. The experimental results and analyses indicate that the proposed MCVSI outperforms many existing mainstream VSI in terms of higher performance and stronger robustness, particularly on low and medium quality images.

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“…Therefore, the visual quality issues of ODIs are much more acute and challenging, calling for dedicated ODI quality evaluation metrics. During the past decades, image quality evaluation has been widely investigated and can be roughly categorized into three categories including full-reference (FR), reduced-reference (RR), and no-reference (NR) according to the participation amount of information from reference image (i.e., without distortion) ( Mittal et al, 2013 ; Zhu et al, 2019 ; Liu et al, 2021 ). Compared with the FR/RR methods which require full/partial reference information, the NR methods do not require any information from the reference image, which can have much wider applications in practical systems.…”

Section: Related Workmentioning

confidence: 99%

Dynamically attentive viewport sequence for no-reference quality assessment of omnidirectional images

Wang

Jiang

2022

Front. Neurosci.

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Omnidirectional images (ODIs) have drawn great attention in virtual reality (VR) due to the capability of providing an immersive experience to users. However, ODIs are usually subject to various quality degradations during different processing stages. Thus, the quality assessment of ODIs is of critical importance to the community of VR. The quality assessment of ODIs is quite different from that of traditional 2D images. Existing IQA methods focus on extracting features from spherical scenes while ignoring the characteristics of actual viewing behavior of humans in continuously browsing an ODI through HMD and failing to characterize the temporal dynamics of the browsing process in terms of the temporal order of viewports. In this article, we resort to the law of gravity to detect the dynamically attentive regions of humans when viewing ODIs. In this article, we propose a novel no-reference (NR) ODI quality evaluation method by making efforts on two aspects including the construction of Dynamically Attentive Viewport Sequence (DAVS) from ODIs and the extraction of Quality-Aware Features (QAFs) from DAVS. The construction of DAVS aims to build a sequence of viewports that are likely to be explored by viewers based on the prediction of visual scanpath when viewers are freely exploring the ODI within the exploration time via HMD. A DAVS that contains only global motion can then be obtained by sampling a series of viewports from the ODI along the predicted visual scanpath. The subsequent quality evaluation of ODIs is performed merely based on the DAVS. The extraction of QAFs aims to obtain effective feature representations that are highly discriminative in terms of perceived distortion and visual quality. Finally, we can adopt a regression model to map the extracted QAFs to a single predicted quality score. Experimental results on two datasets demonstrate that the proposed method is able to deliver state-of-the-art performance.

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Blind Omnidirectional Image Quality Assessment Based on Structure and Natural Features

Cited by 21 publications

References 55 publications

Multifaceted perception-based blind omnidirectional image quality assessment

Multifaceted perception-based blind omnidirectional image quality assessment

Visual security index of selective encrypted images based on multi-directional structure and content-aware features

Dynamically attentive viewport sequence for no-reference quality assessment of omnidirectional images

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