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.

本文旨在通过一项有关画质评估的研究，建立大规模的虚拟现实（VR）主观质量数据库，以发展 VR 品质模型和基准测试 VR 品质预测算法。研究中，使用 450 张受损的图像进行了调查，并记录了主观评分和眼动数据，支持了注视方向与品质感知之间的关系。文章还对几个公开可用的 IQA 模型进行了评估，并对比了它们的性能。

3D 虚拟现实画面质量研究

Study of 3D Virtual Reality Picture Quality

Video Quality Assessment (VQA) is a very challenging task due to its highly
subjective nature. Moreover, many factors influence VQA. Compression of video
content, while necessary for minimising transmission and storage requirements,
introduces distortions which can have detrimental effects on the perceived
quality. Especially when dealing with modern video coding standards, it is
extremely difficult to model the effects of compression due to the
unpredictability of encoding on different content types. Moreover, transmission
also introduces delays and other distortion types which affect the perceived
quality. Therefore, it would be highly beneficial to accurately predict the
perceived quality of video to be distributed over modern content distribution
platforms, so that specific actions could be undertaken to maximise the Quality
of Experience (QoE) of the users. Traditional VQA techniques based on feature
extraction and modelling may not be sufficiently accurate. In this paper, a
novel Deep Learning (DL) framework is introduced for effectively predicting VQA
of video content delivery mechanisms based on end-to-end feature learning. The
proposed framework is based on Convolutional Neural Networks, taking into
account compression distortion as well as transmission delays. Training and
evaluation of the proposed framework are performed on a user annotated VQA
dataset specifically created to undertake this work. The experiments show that
the proposed methods can lead to high accuracy of the quality estimation,
showcasing the potential of using DL in complex VQA scenarios.

本文介绍一种基于深度学习框架的视频质量评估方法，综合考虑了压缩失真和传输延迟，通过端到端特征学习，可提高视频内容传输机制 VQA 的准确性。