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360度视频与视口预测方法综述

李镇淮 战荫伟

计算机工程与应用2024,Vol.60Issue(2):19-31,13.
计算机工程与应用2024,Vol.60Issue(2):19-31,13.DOI:10.3778/j.issn.1002-8331.2305-0056

360度视频与视口预测方法综述

Overview of 360-Degree Video and Viewport Prediction

李镇淮 1战荫伟1

作者信息

  • 1. 广东工业大学 计算机学院,广州 510006
  • 折叠

摘要

Abstract

360-degree video is one of the convenient media to obtain immersive virtual reality experience,which has attracted wide attention in recent years.Viewport prediction technology is an important means to alleviate the high bandwidth requirement of 360-degree video network,focusing viewport prediction technique,the basic concept of 360-degree video,background and 360-degree video streaming framework are firstly introduced,and the common sphere to plane projection methods and video codec standards are compared.The disadvantage of 360-degree video high network resource consump-tion is analyzed,and the important role of viewport prediction technology for video streaming is introduced.The 360-degree attention dataset is introduced,and the mainstream public datasets are summarized.The existing viewport prediction meth-ods are divided into user history track based method and video content-based method,and a systematic review is carried out to sort out the development of viewport prediction methods,introduce the latest work of viewport prediction methods,compare the characteristics,advantages and limitations of different methods,and briefly introduce the 360-degree image salient detection.360-degree salient detection is the focus of viewport prediction method based on video content.Finally,the problems faced by viewport prediction methods are analyzed,and the future development trend of 360-degree video related technologies including viewport prediction methods is forecasted.

关键词

360度视频/全景视频/视口预测/显著性检测/深度学习/虚拟现实

Key words

360-degree video/panoramic video/viewport prediction/saliency detection/deep learning/virtual reality

分类

信息技术与安全科学

引用本文复制引用

李镇淮,战荫伟..360度视频与视口预测方法综述[J].计算机工程与应用,2024,60(2):19-31,13.

基金项目

广东省重点领域研发计划项目(2019B010150002,2020B0101130019). (2019B010150002,2020B0101130019)

计算机工程与应用

OA北大核心CSTPCD

1002-8331

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