Netflix 在發了一篇很長的文章，說明怎麼評估 video quality：「Toward A Practical Perceptual Video Quality Metric」，文章雖然有點長，但其實還蠻好懂的...
講的白話一點，Netflix 想要做各種壓縮方式的改善，但在超大的量的情況下 (scale) 缺乏自動化打分數的機制：
All of the challenging work described above hinges on one fundamental premise: that we can accurately and efficiently measure the perceptual quality of a video stream at scale.
如果先不考慮 scale 問題，影片的評估方式有人工處理以及常見的計算方法 (像是 MSE、PSNR 與 SSIM)：
Traditionally, in video codec development and research, two methods have been extensively used to evaluate video quality: 1) Visual subjective testing and 2) Calculation of simple metrics such as PSNR, or more recently, SSIM.
前者因為牽涉到人工，所以不 scale，而後者跟「人的觀感」還是不夠正相關：
Without doubt, manual visual inspection is operationally and economically infeasible for the throughput of our production, A/B test monitoring and encoding research experiments.
Although researchers and engineers in the field are well-aware that PSNR does not consistently reflect human perception, it remains the de facto standard for codec comparisons and codec standardization work.
Netflix 的作法其實很簡單：(但是每一步都很仔細)
- 首先先把影片依照手上有的 metadata 歸類，然後再挑出代表性的剪輯，並且產生不同 bitrate 的檔案。
- 用人工對這些剪輯評分。
- 用機器產生各種既有計算方法的分數 (PSNR、SSIM、...)。
- 用數學方法把人工的與機器算的分數建立 model。
- 然後對於未知的影片先寄算出既有方法的分數 (PSNR、SSIM、...)，然後套用 model 推估人的觀感。
沒什麼特別發明出來的演算法，只是苦工 XDDD
