We present a new methodology that allows for more objective comparison of video codecs, using the recently published Dynamic Optimizer framework. We show how this methodology is relevant primarily to non-real time encoding for adaptive streaming applications and can be applied to any existing and future video codecs. By using VMAF, Netflix’s open-source perceptual video quality metric, in the dynamic optimizer, we offer the possibility to do visual perceptual optimization of any video codec and thus produce optimal results in terms of PSNR and VMAF. We focus our testing using full-length titles from the Netflix catalog. We include results from practical encoder implementations of AVC, HEVC and VP9. Our results show the advantages and disadvantages of different encoders for different bitrate/quality ranges and for a variety of content.