how to use the Netflix open source Meridian movie files

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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How accurate are your Golden Eyes?

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 to learn by doing. There is an incentive to participate which we talk about in the last paragraph.

a video picture with distortion

What is the quality of this video picture?

Measuring Video Quality

Professionals who build systems around video understand that being able to measure video picture quality is a key differentiator.  Streaming media service providers want to deliver content to viewers with just the right quality.  If it is too good it means excess bandwidth and cost was used to deliver it. If the quality is poor it can have negative effects on brand reputation and subscriber attrition. Either way it is a primary concern for many businesses.

Methods for evaluating video quality can be divided into three categories.

  1. Golden Eyes.  Expert viewers who evaluate video based on their experience and judgement
  2. Standards-based subjective video quality assessments.  These provide a definitive measurement of quality to which the other methods can be compared for accuracy, but they are labor intensive because they involve human evaluators.
  3. Objective computational models that can be automated for off-line or operational assessment. There are many algorithms available and more under development, all striving to compute quality or impairment on a scale that is meant to reflect human perception

Note that these methods are concerned with evaluation of impairments due to the various video processing steps for distribution, which may add subtle (or worse) blur, compression artifacts, or noise.   Severe quality of experience issues such as interruptions in delivery or gross picture impairments caused by dropped network packets are also important but are outside the scope of this article.

Subjective Evaluation Standards

subjective assessment

A subjective assessment test session

The standards-based subjective assessment approach has evolved from its standard definition, interlaced broadcast roots.  Recommendation ITU-R BT.500-13 [1] is the most widely referenced subjective assessment standard.  It provides guidelines for video material selection, viewing conditions, observer training, double-stimulus and single-stimulus test session sequences, and analysis of collected scores. Test sessions were traditionally conducted in controlled laboratory or studio environments, with groups or individual observers, under the supervision of a moderator.

More recent standards such as Recommendation ITU-T P.913 [2] provide updated conditions and methods more suitable for modern UHD displays, and streaming media distribution, and computer support for video playback and test session management.

The objective of the test session is to present a number of video sequences to human observers and collect quality assessment scores.  A five point quality scale is commonly used:

  • 5 – Excellent
  • 4 – Good
  • 3 – Fair
  • 2 – Poor
  • 1 – Bad or Unacceptable

When these scores are averaged over many observers, the result is the Mean Opinion Score (MOS) for each video sequence.  The test session guidelines from the ITU recommendations provide the basis to define MOS as the measured quality.

mean opinion scores

Mean Opinion Scores and 95% confidence interval, from subjective assessment

The subjective evaluation approach is not scalable for operational use, but it does provide the ground truth needed to validate objective quality models that are scalable.  For example, the VQEG Report [3] on quality models for HD video describes a rigorous subjective validation process that was conducted in multiple labs worldwide using methods based on Recommendation BT.500.  Most objective models use some subjective evaluation assessment approach to demonstrate accuracy.

Modern Subjective Evaluation as a Web Application

A subjective assessment campaign is currently being conducted as a web application.  The test session method is the ACR-HR (Absolute Category Rating with Hidden Reference) method described in Recommendation P.913.  Subject videos have been impaired by various processing operations including scaling, compression, and filtering.  Original undistorted videos are also included for scoring in the test session.   During analysis the scores from the impaired stimuli can be subtracted from the score of the corresponding hidden reference to result in a differential score.  The averaged Differential Mean Opinion Score (DMOS) is another useful metric for situations where the reference video is available.

The web-based subjective assessment application implements the specifications of Recommendation BT.500 and P.913 as much as possible.  For example, there are instructions for controlling ambient room illumination and display calibration.  An observer training sequence is presented to anchor the range of impairments represented by the five point grading scale.  As allowed in the recommendation, each video sequence is represented in the test session by a single still frame.  This allows the test session to proceed more quickly, although it prevents evaluation of certain impairments such as flicker.

Now try it

Go to to participate. It’s free. The current campaign is open now through March 6, 2018 March 15, 2018.

The site presented the test and pre-test in a appealing and professional manner
– a recent participant

Your Incentive

As a reward for your time, you will receive a personalized report in a few weeks that will tell how your scores compared to the mean opinion scores.  Were you more generous or more critical in your scoring than the average observer? Were your scores more or less consistent than the others? Maybe you can proclaim yourself to be the “Golden Eyes” of your organization based on this report.


[1] ITU-R BT.500-13, “Methodology for the subjective assessment of the quality of television pictures,” International Telecommunication Union, Geneva, 1/2012, 44p.

[2] ITU-T P.913(01/2014), “Methods for the subjective assessment of video quality, audio quality and audiovisual quality of Internet video and distribution quality television in any environment,” International Telecommunication Union, Geneva, 1/2014, 25p.

[3] Video Quality Experts Group, “Report on the Validation of Video Quality Models for High Definition Video Content,”, Version 2.0, June 30, 2010, 93p.

who says that video looks good?

Short version: please go to 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?



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  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.

after six months . . .

IMG_2361Some decades ago I arrived in Portland to relaunch an engineering career after a short, mistaken move to Westchester County, New York.  One of my first actions getting set up in Oregon was to build my own desk.  There was no money to buy one because I had just returned money for moving expenses to my previous employer.

This desk is still my “daily driver” at Cascade Stream HQ.  It is a constant in a professional evolution full of challenges, accomplishments, and setbacks too, like anyone’s career.  Another constant is my enjoyment of the practice of engineering development, creating solutions from innovation, or at least concentrated time sitting at that desk working to make a design as simple as it can be to get the job done.

Cascade Stream is now six months old, according to state business registration records.  But its real origin dates back to the mid 2000’s as some readers will recall: we talked about this.  We launched last year (and not 7 or 10 years ago) because it was a moment when two clients were open to working with a new company.  They are

  • A leading company in test and measurement
  • A semiconductor manufacturer who has redefined low power, high performance computing with a massively parallel processor architecture

These relationships have allowed us to flex our capabilities in the video domain and accomplish some interesting objectives.

The last six months have also included time spent at the College of Engineering and Computer Science at Portland State University learning about software development for web applications.  This is another tool in the toolbox to enable cloud-based solutions for video streaming applications.

I’m in school again now, this time at the Portland Community College Small Business Development Center, working on this business.

The next six months will bring new clients and new projects. I will make another trip to the NAB convention in April after countless past visits, but the first for Cascade Stream.  The drive for interesting new projects in video keeps it fresh and alive.

Thanks for reading, and thank you for your support.


understanding CIE 1931 chromaticity

The International Telecommunications Union publishes Recommendation ITU-R BT.709 which specifies how image data is coded for transmission as video.  This includes a specification for the representation of color which applies to almost all modern video transmission including broadcast, cable/satellite, or over the internet (but not including emerging practices for  high dynamic range/wide color gamut).

We conventionally understand any color to be a combination of red, green, and blue primaries, but the photoreceptors in the human eye do not function on the basis of these primaries, and BT.709 does not include the first principles needed to understand color representation.  It only offers the following specification:

item parameter system value
1.3 Chromaticity coordinates
(CIE, 1931)
– Red (R)
– Green (G)
– Blue (B)
x y
1.4 Assumed chromaticity for
equal primary signals
(Reference white)
x y
0.3127 0.3290

In this post, we take a deeper look at Publication No. 15 of the Commission Internationale de l’Eclairage, CIE 15:2004, which includes the 1931 chromaticity specification, still the defacto standard for quantifying color in a camera and display device-invariant way.

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phase correlation – part 1

Motion estimation in video processing refers to finding a sub-block in a reference picture that most accurately resembles or predicts a block in a target picture. The horizontal and vertical displacement between the target block and the reference block is represented by a two dimensional motion vector.

Spatial domain block matching techniques are most commonly used for motion estimation in video compression encoders and other video processing applications. They work by computing a distance criterion value between a candidate reference block and the target block, according to some candidate motion vector.  A common distance criterion function is sum of absolute differences (SAD).  The computations need to be repeated for every candidate motion vector, and the one with the smallest distance criterion value is taken as the correct motion vector.

The phase correlation approach uses a frequency domain transformation to find the motion vector in a single iteration.  In some applications it could obtain the motion vector with less computations than the spatial domain block matching approach.  It may also find the motion vector more accurately when there are extraneous differences between target and reference, such as a different illumination levels or image noise.

We studied motion estimation by phase correlation and wrote a notebook article on it, which you can see on this Jupyter notebook page.

We started with a known test image from which we could see the true motion.  Here are the first two frames of the pedestrian_area test clip:


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