Perceptual Quality Assessment of Virtual Reality Videos in the Wild

• URL: http://arxiv.org/abs/2206.08751v3
• Date: Fri, 15 Mar 2024 14:16:40 GMT
• Title: Perceptual Quality Assessment of Virtual Reality Videos in the Wild
• Authors: Wen Wen, Mu Li, Yiru Yao, Xiangjie Sui, Yabin Zhang, Long Lan, Yuming Fang, Kede Ma,
• Abstract summary: Existing panoramic video databases only consider synthetic distortions, assume fixed viewing conditions, and are limited in size. We construct the VR Video Quality in the Wild (VRVQW) database, containing $502$ user-generated videos with diverse content and distortion characteristics. We conduct a formal psychophysical experiment to record the scanpaths and perceived quality scores from $139$ participants under two different viewing conditions.
• Score: 53.94620993606658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Investigating how people perceive virtual reality (VR) videos in the wild (i.e., those captured by everyday users) is a crucial and challenging task in VR-related applications due to complex authentic distortions localized in space and time. Existing panoramic video databases only consider synthetic distortions, assume fixed viewing conditions, and are limited in size. To overcome these shortcomings, we construct the VR Video Quality in the Wild (VRVQW) database, containing $502$ user-generated videos with diverse content and distortion characteristics. Based on VRVQW, we conduct a formal psychophysical experiment to record the scanpaths and perceived quality scores from $139$ participants under two different viewing conditions. We provide a thorough statistical analysis of the recorded data, observing significant impact of viewing conditions on both human scanpaths and perceived quality. Moreover, we develop an objective quality assessment model for VR videos based on pseudocylindrical representation and convolution. Results on the proposed VRVQW show that our method is superior to existing video quality assessment models. We have made the database and code available at https://github.com/limuhit/VR-Video-Quality-in-the-Wild.

Related papers

• AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results [120.95863275142727]
  This paper presents the results of the Compressed Video Quality Assessment challenge, held in conjunction with the Advances in Image Manipulation (AIM) workshop at ECCV 2024. The challenge aimed to evaluate the performance of VQA methods on a diverse dataset of 459 videos encoded with 14 codecs of various compression standards.
  arXiv  Detail & Related papers  (2024-08-21T20:32:45Z)
• WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models [132.77237314239025]
  Video virtual try-on aims to generate realistic sequences that maintain garment identity and adapt to a person's pose and body shape in source videos. Traditional image-based methods, relying on warping and blending, struggle with complex human movements and occlusions. We reconceptualize video try-on as a process of generating videos conditioned on garment descriptions and human motion. Our solution, WildVidFit, employs image-based controlled diffusion models for a streamlined, one-stage approach.
  arXiv  Detail & Related papers  (2024-07-15T11:21:03Z)
• KVQ: Kwai Video Quality Assessment for Short-form Videos [24.5291786508361]
  We establish the first large-scale Kaleidoscope short Video database for Quality assessment, KVQ, which comprises 600 user-uploaded short videos and 3600 processed videos. We propose the first short-form video quality evaluator, i.e., KSVQE, which enables the quality evaluator to identify the quality-determined semantics with the content understanding of large vision language models.
  arXiv  Detail & Related papers  (2024-02-11T14:37:54Z)
• Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment [55.65173181828863]
  We propose a temporal perceptual quality index (TPQI) to measure the temporal distortion by describing the graphic morphology of the representation. Experiments show that TPQI is an effective way of predicting subjective temporal quality.
  arXiv  Detail & Related papers  (2022-07-08T07:30:51Z)
• Perceptual Quality Assessment of Omnidirectional Images [81.76416696753947]
  We first establish an omnidirectional IQA (OIQA) database, which includes 16 source images and 320 distorted images degraded by 4 commonly encountered distortion types. Then a subjective quality evaluation study is conducted on the OIQA database in the VR environment. The original and distorted omnidirectional images, subjective quality ratings, and the head and eye movement data together constitute the OIQA database.
  arXiv  Detail & Related papers  (2022-07-06T13:40:38Z)
• Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception [32.87570883484805]
  We propose to transfer knowledge from image quality assessment (IQA) databases with authentic distortions and large-scale action recognition with rich motion patterns. We train the proposed model on the target VQA databases using a mixed list-wise ranking loss function.
  arXiv  Detail & Related papers  (2021-08-19T05:29:19Z)
• Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training [20.288424566444224]
  We focus on automatically assessing the quality of in-the-wild videos in computer vision applications. To improve the performance of quality assessment models, we borrow intuitions from human perception. We propose a mixed datasets training strategy for training a single VQA model with multiple datasets.
  arXiv  Detail & Related papers  (2020-11-09T09:22:57Z)
• Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos [49.217528156417906]
  Two types of VR viewing conditions are crucial in determining the viewing behaviors of users and the perceived quality of the panorama. We first transform an omnidirectional image to several video representations using different user viewing behaviors under different viewing conditions. We then leverage advanced 2D full-reference video quality models to compute the perceived quality.
  arXiv  Detail & Related papers  (2020-05-21T10:03:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.