Perceptual Quality Assessment of Omnidirectional Images

Duan, Huiyu; Zhai, Guangtao; Min, Xiongkuo; Zhu, Yucheng; Fang, Yi; Yang, Xiaokang

doi:10.1109/iscas.2018.8351786

Cited by 115 publications

(65 citation statements)

References 19 publications

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“…The resolution of these images is 4096 × 2048. The OIQA [117] also contains 16 pristine images and 336 distorted images which are perturbed by JPEG and JPEG2000 compressions, Gaussian blur, and white Gaussian noise. The resolutions vary from 11332 × 5666 to 13320 × 6660.…”

Section: B) Qoe Tasks On Hmd Devicementioning

confidence: 99%

Modern trends on quality of experience assessment and future work

Kim

Ahn

Nguyen

et al. 2019

SIP

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Over the past 20 years, research on quality of experience (QoE) has been actively expanded even to cover aesthetic, emotional and psychological experiences. QoE has been an important research topic in determining the perceptual factors that are essential to users in keeping with the emergence of new display technologies. In this paper, we provide in-depth reviews of recent assessment studies in this field. Compared to previous reviews, our research examines the human factors observed over various recent displays and their associated assessment methods. In this study, we first provide a comprehensive QoE analysis on 2D display including image/video quality assessment (I/VQA), visual preference, and human visual system-related studies. Second, we analyze stereoscopic 3D (S3D) QoE research on the topics of I/VQA and visual discomfort from the human perception point of view on S3D display. Third, we investigate QoE in a head-mounted display-based virtual reality (VR) environment, and deal with VR sickness and 360 I/VQA with their individual approach. All of our reviews are analyzed through comparison of benchmark models. Furthermore, we layout QoE works on future display and modern deep-learning applications.

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Section: B) Qoe Tasks On Hmd Devicementioning

confidence: 99%

Modern trends on quality of experience assessment and future work

Kim

Ahn

Nguyen

et al. 2019

SIP

View full text Add to dashboard Cite

show abstract

“…To specifically assess the visual quality of panoramic images, numerous stitched image quality assessment (SIQA) methods have been presented in the past decade. Among the diversity of the stitching literature, a number of various researchers focus on the quality assessment of the stitched images by either using conventional handcrafted features based [ 21 , 22 ] methods or making subjective comparisons [ 23 , 24 , 25 ]. Broadly, the area of stitch image quality assessment (SIQA) is different from classical IQA in two perspectives.…”

Section: Introductionmentioning

confidence: 99%

DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

Ullah

Irfan

Han

et al. 2020

Sensors

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Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning in various domains of artificial intelligence has extended the attention of researchers to contribute to different fields of computer vision. To ensure the quality of immersive media contents using these advanced deep learning technologies, several learning based Stitched Image Quality Assessment methods have been proposed with reasonable performances. However, these methods are unable to localize, segment, and extract the stitching errors in panoramic images. Further, these methods used computationally complex procedures for quality assessment of panoramic images. With these motivations, in this paper, we propose a novel three-fold Deep Learning based No-Reference Stitched Image Quality Assessment (DLNR-SIQA) approach to evaluate the quality of immersive contents. In the first fold, we fined-tuned the state-of-the-art Mask R-CNN (Regional Convolutional Neural Network) on manually annotated various stitching error-based cropped images from the two publicly available datasets. In the second fold, we segment and localize various stitching errors present in the immersive contents. Finally, based on the distorted regions present in the immersive contents, we measured the overall quality of the stitched images. Unlike existing methods that only measure the quality of the images using deep features, our proposed method can efficiently segment and localize stitching errors and estimate the image quality by investigating segmented regions. We also carried out extensive qualitative and quantitative comparison with full reference image quality assessment (FR-IQA) and no reference image quality assessment (NR-IQA) on two publicly available datasets, where the proposed system outperformed the existing state-of-the-art techniques.

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“…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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Perceptual Quality Assessment of Omnidirectional Images

Cited by 115 publications

References 19 publications

Modern trends on quality of experience assessment and future work

Modern trends on quality of experience assessment and future work

DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

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

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