Deep Virtual Reality Image Quality Assessment With Human Perception Guider for Omnidirectional Image

Kim, Hak Gu; Lim, Heoun-taek; Ro, Yong Man

doi:10.1109/tcsvt.2019.2898732

Cited by 136 publications

(64 citation statements)

References 38 publications

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“…In the scene of human-computer interaction, virtual reality technology provides a new mode for space performance, and also brings new innovation. At the same time, it can help us to explore the laws of movement and change in the macro and micro world, as well as the movement and change laws of things that are not easy to observe directly due to various reasons [10].…”

Section: A Vr Technologymentioning

confidence: 99%

Visual Error Correction Method for VR Image of Continuous Aerobics

Zhang

2024

IEEE Access

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In recent years, with the rapid development of virtual reality technology, the visual error correction of continuous aerobics image in virtual environment is of great value to the analysis of Aerobics technology. The main purpose of this paper is to study the visual error correction algorithm of VR image of continuous aerobics. This paper mainly introduces the overview of VR technology, characterist ics and aerobics, some algorithms of visual error correction. This article selects a continuous aerobics coach to take a continuous action of aerobics. These photos were modeled by VR, and the VR images of continuous aerobics were made. The visual error correction of X, y, depth direction of continuous aerobics action VR image with savd algorithm, it algorithm and FT algorithm proposed in this paper are compared, and the VR image performance of savd algorithm and it algorithm is compared. The experimental results show that: the errors of savd algorithm, it algorithm and FT algorithm in X direction, Y direction and depth direction of continuous aerobics action VR image without error correction are reduced by 41%, 26%, 14.16%, 31%, 29%, 21%, 72.7%, 41.1%, 35.1%, respectively. In this paper, the effect of savd algorithm in X direction and depth direction error correction is very obvious, in Y direction error correction has some effect, but not obvious, savd algorithm in continuous aerobics action VR image visual error correction effect is better than it algorithm and FT algorithm. The correctness and feasibility of the proposed savd algorithm for visual error correction of continuous aerobics action VR image is verified.

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Section: A Vr Technologymentioning

confidence: 99%

Visual Error Correction Method for VR Image of Continuous Aerobics

Zhang

2024

IEEE Access

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“…For example, in [30], the authors deployed an end-to-end 3D convolutional neural network to predict the quality of VR videos without reference. In [31] and [32], the power of adversarial learning was utilized to successfully predict the quality of images.…”

Section: B Objective Quality Assessmentmentioning

confidence: 99%

Study of 3D Virtual Reality Picture Quality

Chen

Jin

Goodall

et al. 2020

IEEE J. Sel. Top. Signal Process.

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Virtual Reality (VR) and its applications have attracted significant and increasing attention. However, the requirements of much larger file sizes, different storage formats, and immersive viewing conditions pose significant challenges to the goals of acquiring, transmitting, compressing and displaying high quality VR content. Towards meeting these challenges, it is important to be able to understand the distortions that arise and that can affect the perceived quality of displayed VR content. It is also important to develop ways to automatically predict VR picture quality. Meeting these challenges requires basic tools in the form of large, representative subjective VR quality databases on which VR quality models can be developed and which can be used to benchmark VR quality prediction algorithms. Towards making progress in this direction, here we present the results of an immersive 3D subjective image quality assessment study. In the study, 450 distorted images obtained from 15 pristine 3D VR images modified by 6 types of distortion of varying severities were evaluated by 42 subjects in a controlled VR setting. Both the subject ratings as well as eye tracking data were recorded and made available as part of the new database, in hopes that the relationships between gaze direction and perceived quality might be better understood. We also evaluated several publicly available IQA models on the new database, and also report a statistical evaluation of the performances of the compared IQA models.Index Terms-image quality assessment, immersive image database, virtual reality, human perception, full reference.

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“…Learning-based I/VQA methods: Given the popularity of deep learning, recent works also manage to benefit from the phenomenal performance of CNN. Kim et al [128] proposed to extract patch-based positional and visual features from a 360 image using CNN, and then regressed the features onto the ground truth MOS. Similarly, Lim et al [129] proposed latent spatial and positional features, and used adversarial learning to predict the quality score.…”

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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Deep Virtual Reality Image Quality Assessment With Human Perception Guider for Omnidirectional Image

Cited by 136 publications

References 38 publications

Visual Error Correction Method for VR Image of Continuous Aerobics

Visual Error Correction Method for VR Image of Continuous Aerobics

Study of 3D Virtual Reality Picture Quality

Modern trends on quality of experience assessment and future work

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