Defend Your Video Encoding Choices with Data

I have two very strong memories from my fraternity days in college. One relates to the night I learned how to play the drinking game Whales Tales, which I won't share. 第二张是兄弟会会议, which the chapter president started by saying, "The biggest problem facing our fraternity is apathy." The memorable response from one member: a loud, "Aw, 谁在乎?" 

That response came to mind this week when I read a tweet commenting on my blog post, "How to Reduce Encoding Time by Up to 40% With Negligible Quality Loss." The post shows that adding reference frames to an x264 encode could boost encoding time by 40% or more, 质量改进很少. 在一个数据集中使用非常慢的预设, jumping from one reference frame to 16 improved Video Multimethod Assessment Fusion (VMAF) quality from 95.97 to 96.06，但将编码时间提高了37.12%. 

为视角, it takes about six VMAF points to equal a just noticeable difference, 所以即使是最能干的, 敏锐的观察者不会注意到0.09个VMAF点. However, even a junior accountant would notice a 37% decrease in encoding costs. 

我用10个不同的文件进行了测试, 至少2分钟, and with two different presets (medium and veryslow) and testing reference frame settings of 1, 3, 4, 8, 10, 和16. I customized data rates for each file to achieve a VMAF score of around 93–96, which is a reasonable target for the top rung of an encoding ladder. 

The blog post incorporated about 3 days of testing, which involved around 400 encodes and VMAF calculations along with results visualizations using the Moscow State University 视频质量 Measurement Tool (VQMT). Note that I did qualify my findings by stating that results with different H.264 codecs might vary, or potentially even different encoders using the x264 codec (I used FFmpeg). 关键是，不要想当然. 用你的装置做几个测试, 如果你取得了类似的结果, you might just save your company some serious coin. 

What was the "frat boy" tweet in response to my post? 几天后, 有人在推特上, "Number of reference frames between 4 and 8 should be optimal." So, 他对我那篇数据驱动的文章的回应是, 在本质上, 他的观点比数据更重要. 

这有什么意义?? If you're charged with configuring your encoding ladder, there are a few datapoints you should have nailed. The most important is the VMAF score of the top rung of your encoding ladder. 如果平均超过96, you should reduce the data rate of what typically is one of your most highly consumed files. 在一个咨询项目中, this analysis allowed a large OTT client to drop the data rate for talk shows and game shows from 8Mbps to 5Mbps without any customer noticing. Other content, like sports and action shows, needed the 8Mbps to achieve the necessary quality. 

You should know the encoding time/quality trade-off of the encoding preset that you're using. In my x264 tests, the difference between the faster and veryslow preset averaged 1.1 VMAF points, while the higher-quality preset took four 倍的时间. You should know the same trade-off for reference frames and other details. 

The only practical way to do this is with video quality metrics like VMAF, SSIMPLUS, and some others. I know it's trendy to dismiss video quality metrics as imprecise, but that's making perfect the enemy of the good. Metrics are used by developers to benchmark codecs and run multiple per-title encoding engines, inclu-ding Netflix的. 专有的质量度量是 
Beamr的Content-Adaptive引擎 Rate Control (CABR) and encoders from Bright­cove, ATEME, and many others. With tools like VQMT and SSIMPLUS VOD Monitor, you can use metrics to identify trouble spots and verify the score with your own eyes. 当通过有效的可视化工具交付时, 参数不仅仅是一个数字, they're a road map to quality differences in your video files. Or, in the case of reference frames, the lack of meaningful quality differences. 

相信我, if your boss ever asks for data to back up your encoding configurations, the last things you'll want to say are "My opinion matters more than data" or, 特别是, "Aw, 谁在乎?"

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