Image and video quality assessment

Related tags

VideoCenseoQoE
Overview

CenseoQoE: 视觉感知画质评价框架

PRs Welcome

项目介绍

图像/视频在编解码、传输和显示等过程中难免引入不同类型/程度的失真导致图像质量下降。图像/视频质量评价(IVQA)的研究目标是希望模仿人类视觉感知系统, 通过算法评估图片/视频在终端用户的眼中画质主观体验的好坏,目前在视频编解码、画质增强、画质监控、推荐算法、竞品分析等领域有广泛的应用。

CenseoQoE 提供图像/视频画质评价算法模型训练到应用落地的完整方案,主要包括 CenseoQoE-AlgorithmCenseoQoE-SDK两部分。 CenseoQoE-Algorithm 是通用的画质评价算法模型的训练框架,CenseoQoE-SDK集成了针对多种业务场景训练好的画质评价模型,可直接对UGC视频、PGC视频、游戏视频等做无参考/有参考的画质评价,实现开箱即用。

为什么叫CenseoQoECenseo 在拉丁语中是我认为/我觉得的意思,与图像/视频画质体验评价领域的主观评测概念相符,QoE则是体验质量(Quality of Experience)。

主要特性

  • CenseoQoE-Algorithm 提供图像/视频画质评价算法模型训练的codebase,集成自研的模型以及业界开源的算法模型,支持无参考和有参考。此外,我们会公开不同业务场景下 (UGC视频、PGC视频、游戏视频、直播视频等)的预训练模型,用户可以利用这些预训练模型在自己的数据集进行微调优化。
  • CenseoQoE-SDK 集成了基于CenseoQoE-Algorithm在我们自研的数据集上训练得到的多个业务场景下的画质评价模型,全部使用c++实现,编译之后可直接 对UGC视频、PGC视频、游戏视频等做画质评价,实现开箱即用。

快速开始

  • 如果你想做模型的训练或是优化改进,请参见CenseoQoE-Algorithm
  • 如果你想快速使用CenseoQoE推出的画质评价工具/SDK,请参见CenseoQoE-SDK

Demo

以下是我们针对游戏视频训练的两个模型(resnet-based和shufflenet-based)和vmaf的两个模型(vmaf-default和vmaf-phone)在同一个视频上的质量分数对比。

our model vs vmaf

常见问题

请参考 FAQ 了解其他用户的常见问题。

许可

该项目使用的开源协议为 MIT

引用

@misc{wen2021strong,
      title={A strong baseline for image and video quality assessment}, 
      author={Shaoguo Wen and Junle Wang},
      year={2021},
      eprint={2111.07104},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

参与贡献

我们非常欢迎用户对于 CenseoQoE 做出的任何贡献,可以参考 CONTRIBUTION.md 文件了解更多细节。

致谢

我们感谢所有为该项目提供建议、数据、模型、使用样例和新功能支持的贡献者,以及提供宝贵反馈的用户。

Owner
Tencent
Tencent
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