Promoting Fluency of Streaming Video by Learning Human Perceptive Traits to Reveal the Vital Section in Outstanding Quality

机译：通过学习人类的感知性状来促进流利的流畅性，以揭示卓越品质的重要部分

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Currently, the quality of digital media and the quantity of contents are both increasing rapidly. For instance, watching e-sport competitions often suffers from unstable bandwidth, which causes the video to stutter or have a low resolution. In this situation, users will have a negative experience. Many situations can cause problems of congestion in real-time applications or 3D displays. To solve this kind of problem, we attempt to determine an inverse solution according to the path. This project adopts a reverse operation that reduces necessary data but maintains the same quality perception of user experience by utilizing the characteristics of the human vision and brain. To explore our approach, we develop a prototype that changes the resolution of the image according to a user's habit and shows the part in focus clearly while leaving the resolution of the background lower. It selects interested sub-image in pictures and only displays them with higher quality to achieve a lower transmission requirement. This optimization will allow the user experience smoother streaming when there is congestion or unstable situations. Then, we conduct a preliminary user study to investigate some future directions and explore some potential flaws.

机译：目前，数字媒体的质量和内容量均迅速增加。例如，观看电子体育竞赛往往受到不稳定的带宽，这导致视频口吃或具有低分辨率。在这种情况下，用户将产生负面体验。许多情况可能导致实时应用程序或3D显示中拥塞问题。为了解决这种问题，我们试图根据路径确定反向解决方案。该项目采用反向操作，从而减少必要的数据，但通过利用人类视觉和大脑的特征来保持对用户体验的相同质量感知。为了探索我们的方法，我们开发了一种原型，根据用户的习惯，改变了图像的分辨率，并在留下背景的分辨率时清楚地显示焦点。它选择图片中感兴趣的子图像，只能以更高的质量显示它们以实现更低的传输要求。当存在拥塞或不稳定情况时，这种优化将允许用户体验更平滑的流。然后，我们进行初步的用户学习来调查一些未来的方向，并探索一些潜在的缺陷。

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《International Conferences on Advances in Multimedia》|2020年|54p|共5页
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作者
Chiang Shu Chiao; Tatsuo Nakajima;
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原文格式 PDF
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中图分类 TP37-53;
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Image processing; Deep learning; Accelerated streaming; Media data structure;

机译：图像处理;深入学习;加速流媒体;媒体数据结构;

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1. Towards Approximation of Human's Perceptive Space on Photos, Videos and 3D Scenes [J] . Anna Epishkina, Sergey Zapechnikov Procedia Computer Science . 2016,第1期

机译：在照片，视频和3D场景上逼近人类的感知空间
2. Learning to diagnose using patient video case in paediatrics: perceptive and cognitive processes [J] . Thomas Balslev Perspectives on Medical Education . 2012,第5a6期

机译：在儿科学习使用患者视频病例进行诊断：感知和认知过程
3. Mosaic: Advancing User Quality of Experience in 360-Degree Video Streaming With Machine Learning [J] . Park Sohee, Bhattacharya Arani, Yang Zhibo, Network and Service Management, IEEE Transactions on . 2021,第1期

机译：马赛克：通过机器学习推进360度视频流中的用户体验质量
4. Promoting Fluency of Streaming Video by Learning Human Perceptive Traits to Reveal the Vital Section in Outstanding Quality [C] . Chiang Shu Chiao, Tatsuo Nakajima International Conferences on Advances in Multimedia . 2020

机译：通过学习人类的感知性状来促进流利的流畅性，以揭示卓越品质的重要部分
5. Fluency in Real-Time Video Streaming by Learning Human Perceptive Traits to Reveal the Expected Section in Outstanding Quality [D] . Chiang shu chiao 2020

机译：通过学习人类的感知特性来揭示实时部分的流利性，以显示出预期的高质量部分
6. Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming [O] . Frank Loh, Fabian Poignée, Florian Wamser, 2021

机译：上行链路与下行链路：基于机器学习的HTTP自适应视频流的质量预测
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Promoting Fluency of Streaming Video by Learning Human Perceptive Traits to Reveal the Vital Section in Outstanding Quality

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