Meridian, a 2016 Netflix release, is available for uncompressed download for engineers, production artists, or anyone else who wants to practice their media processing skills on studio production quality material.  Many thanks are due to Netflix for releasing this title under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

(CC) Netflix licensed under CC BY-NC-ND 4.0

The film is described as “cerebral, full of suspense, chilling, ominous.” I don’t know about the story, but those words certainly describe the process of obtaining and using this film’s downloadable media assets.  It is released in multiple color renderings on two different sites, some versions in Interoperable Media Format (IMF) and other encodings wrapped in mp4 packages.

You know with a name like “Interoperable Media Format” that the IMF package is likely to present some barriers to actually access the elementary audio and video media.  IMF adds a layer of packaging on top Material eXchange Format (MXF), which by itself is always a challenge to wrap and unwrap.

In this post we’ll enter the Meridian cave armed with only a flashlight and ffmpeg.  When we emerge, we’ll have elementary stream files in hand and know what color rendering they belong to.

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Subjective video quality evaluation involves people making judgments based on observations and experience. Some professional “golden eyes” do this as part of their job.

Now you can test your own skills in a standards-based video quality assessment campaign that is currently being conducted as a web application. Keep reading to learn more about subjective video quality evaluation, or visit https://pqtest.cascadestream.com to learn by doing. There is an incentive to participate which we talk about in the last paragraph.

What is the quality of this video picture?

Short version: please go to https://pqtest.cascadestream.com and participate in a subjective picture quality evaluation test session.  Running now through March 6, 2018.

It’s the evening and the dishes are done. You are relaxing with your favorite person, starting to watch your new favorite drama on your big screen, and Ugh. The picture quality looks terrible! How does this happen? How can we fix it?

Bleck!

Your streaming content service provider actually does care about picture quality. Often the problem is that the process they use to create the compressed video distribution files you see is unattended, and without a human checking every program encoded at every bit rate they simply don’t have the data on picture quality.

You Can Help!

A number of researchers and developers are working on computational models (i.e. automated) that can evaluate video on a human perceptual quality scale.  This is a really hard problem but the algorithms are getting better every year.

These researchers and developers need to know the true quality of video in order for them to improve the accuracy of their models.  But who can tell them the true quality?  Answer: you.

You tell them if it looks good or bad

Cascade Stream is currently hosting a campaign of picture quality evaluation test sessions to determine the true quality of a number of video sequences with various distortions applied to them.  We are seeking volunteers to participate and score the video pictures.  Your scores will be combined with those of many others resulting in Mean Opinion Scores (MOS) that are the true measurement of quality for those video sequences and distortions.  By definition, according to standard recommended practices for subjective video quality evaluation, what you say is what it is.

Do you feel empowered?

You are.  Please be an official observer in our picture quality evaluation campaign.  To participate, just go to https://pqtest.cascadestream.com.  It’s like a focus group that you can do from your home or office.  Everything you need to know is on that site.  The test session takes about half an hour.

Thank you!

This campaign runs through March 6, 2018 so don’t delay.  The library of video pictures, distortions, and quality scores will become a useful resource for developers.

The next time you are watching your favorite new show and it looks great, you may have an automated quality evaluation model that was improved by your scores to thank for it.