判断苹果的高级视频质量工具
A苹果推出了 高级视频质量工具(AVQT) 在2021年全球开发者大会上. 以下是我了解到的概况.
在我的帖子里 流媒体学习中心网站, 我要指出的是，AVQT既是一个质量指标, 比如VMAF和SSIMPLUS, 和一个工具来产生这个分数, like the Moscow State University 视频质量 Measurement Tool (VQMT) or SSIMPLUS VOD Monitor. 作为一种工具, AVQT is free, Mac-only, and command-line-driven. 生成AVQT评分, it compares the encoded file to the source and reports the score in either JSON or CSV format. You can't visualise the frames or score within the tool.
The tool has a number of features not typically seen in first-gen quality tools. For example, it can compare low-res videos to the source without pre-scaling. It can measure quality at different display resolutions, so you can measure the quality of a 640x360 video played in a 640x360 window, 以及全屏. You can also choose the viewing distance and the pooling method.
AVQT supports Dolby Vision 5 if the source and reference videos are in that format, 而且速度非常快. On an 8-core M1-based Mac mini, processing a 2-minute file took 15 seconds. On an 8-core Intel Xeon CPU E3-1505M Windows based-computer, 在同一个文件上计算VMAF花费了8:40, 大约长35倍.
奇怪的是, 然而, the tool computes the score on a segment-by-segment basis, 档案上没有整体记录. You can set the segment size up to 60 seconds and get a single score, but if you're measuring files longer than 60 seconds, 你会得到一段一段的结果
score that you'll have to convert to a total score if needed.
I asked Apple about this and got this response: “We agree there is value in reporting an overall score for the whole video. 然而,
this is quite challenging as it requires subjective data on long duration videos to design and evaluate an aggregation model. The aggregation model would need to mathematically model several memory related aspects in the human visual system such as first and last impressions, sudden quality drops and the length of low-quality periods.”
This “perfect is the enemy of the good” response ignores the fact that every other metric reports a single score and that most of the items referenced have more impact in a short file than a long file. 10秒里糟糕的5秒
second file could destroy my overall opinion; 5 bad seconds in a 90-minute file is irrelevant.
In 另一篇文章, I tested the metric and discovered that AVQT will fail if the encoded durations or frame rates vary from the source by even an irrelevant difference. 在一个测试中，编码文件为10.长0767秒，源是10.07, AVQT failed, even though both files had 300 frames. In contrast, FFmpeg, VQMT, and the SSIMPLUS VOD Monitor had no problems.
You can force AVQT to ignore the difference and produce a score, 但是你要承担分数无效的风险, with no way to determine if there's a misalignment, as you can with VQMT or the SSIMPLUS VOD Monitor.
许多度量应用都涉及到短, pristine YUV files encoded by precise reference encoders, 所以这两个问题都不是限制. If you work with longer, real-world videos encoded via commercial tools, AVQT might be a problem.
A 最后一个帖子 compared the AVQT metric to VMAF and SSIMPLUS, with some subjective evaluations from Subjectify.us. 在这里, 尽管AVQT也有一些亮点, I didn't test enough data to conclude anything other than that AVQT, VMAF, 和SSIMPLUS测量质量不同, so you can't use VQMT as a faster VMAF or a cheaper SSIMPLUS.
作为一种工具, assuming the duration and sync issues aren't showstoppers, AVQT's speed and JSON output make it ideal for production. 对于像我这样以实验为导向的实践, the inability to visualise frames is a significant limitation. 作为一个度规, AVQT也有一些亮点, but it's hard to see it bumping VMAF or SSIMPLUS from real-world workflows without a lot more verification.
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