VMAF FTW
Vimeo has long been known for the high level of video quality that we deliver to our viewers. But how do we verify that? Looking at videos and listening to customer feedback is helpful, but that’s anecdotal evidence that covers only a very small sample of all the videos uploaded to our platform. Wouldn’t it be great if there were some technique that could measure the quality of our videos automatically and give us a metric of how well we’re doing?
长期以来，Vimeo一直以向观众提供高水平的视频质量而闻名。但我们如何验证这一点？观察视频和听取客户的反馈是有帮助的，但这是传闻，只涵盖了上传到我们平台的所有视频中的很小的样本。如果有一些技术可以自动测量我们的视频质量，并给我们一个衡量我们做得如何的标准，那不是很好吗？
Being able to measure the quality of video objectively is such a useful tool that development of such measurements spans back to the earliest days of digital video. The current best performer Netflix has developed exactly the thing: VMAF. This metric combines several previously known metrics via a learned model to produce a composite score that outperforms its individual components. In addition, it’s one of the few metrics designed to create useful results when comparing videos encoded at multiple resolutions.
能够客观地测量视频的质量是一个非常有用的工具，这种测量的发展可以追溯到数字视频的最早时期。目前表现最好的Netflix正是开发了这个东西：VMAF。这个指标通过一个学习模型将以前已知的几个指标结合起来，产生一个综合得分，其表现优于其单个组成部分。此外，它是为数不多的在比较以多种分辨率编码的视频时产生有用结果的指标之一。
In this post, we’ll go over the basics of Vimeo’s encoding pipeline, and how we integrated VMAF computation into it in an efficient way. We’ll also look at several ways to collect and analyze the resulting data, in order to continuously improve the quality of video that Vimeo delivers.
在这篇文章中，我们将介绍Vimeo的编码管道的基本情况，以及我们如何以一种有效的方式将VMAF计算整合到其中。我们还将研究收集和分析所产生的数据的几种方法，以便不断提高Vimeo提供的视频质量。
Looking at the video encoding pipeline at Vimeo
看一看维密的视频编码管道
At Vimeo, when you upload a video, we perform four basic video processing steps:
在Vimeo，当你上传视频时，我们会执行四个基本的视频处理步骤。
- Decoding. Your video is separated from its audio and decompressed from the format that you uploaded, into raw pixel data.
- 解码。你的视频与音频分离，并从你上传的格式中解压，变成原始像素数据。
- Scaling. We encode your video into multiple profiles, each being ...