Protecting a Story’s Future with History and Science

By Kylee Peña, Chris Clark, and Mike Whipple

Kylee’s parents after their wedding in 1978.

I — Kylee — have two photos from my parents’ wedding. Just two. This year they celebrated 40 years of marriage, so both photos were shot on film. Both capture a joy and awkwardness that come with young weddings. They’re fresh and full of life, candid captures from another era.

One of the photos is a Polaroid Instant Camera shot. It’s the only copy of the photo. The colors are beginning to fade, and the image itself is only three or four inches across.

The other was shot on a 35mm camera. My mom and dad stand next to their best man and maid of honor, but the details are lost because the exposure is far too dark. And the negative was lost long ago too.

These are two photos that are important to my history, but I’ll probably never be able to make them any better. One was shot on a lost format, the color and contrast embedded into a single original copy as it was shot. The other could be cleaned up and blown up significantly with modern technology — if only the negative had been handled with care.

Everyone has images that are precious to them: that miraculous video of your dog doing that trick he does. The shot of your grandparents on their final anniversary together. Your own wedding, in which you invested thousands of your own savings. Imagine if you couldn’t play the video anymore, or if your grandparents’ portrait got blurry, or if your skin and wedding dress had a green hue on it.

On a movie or television show, this type of thing happens all the time to the director or cinematographer. They have worked their entire lives to get to the point of capturing that picture, and capturing it correctly. And then later it looks bad and wrong — 24 times a second.

By reaching beyond film and television art and into the realms of history and science, we’ve been working on solving this issue for Netflix projects. The future of our industry always has unknowns thanks in large part to rapidly accelerating innovations in technology. However, we can use what we do know from a hundred years of filmmaking, and the study of human perception, to best preserve content as new technology emerges which allows us to make the experience of viewing it even better. Preserving creative intent while preserving these important images is the goal.

With some attention and care spent up front on building and testing a color managed workflow, a show can look as expected at every point in the process, and archival assets can be created that increase the longevity of a show far into the future and protect it for remastering. The assets we require at Netflix — the NAM, GAM, and VDM — might be digital files that make their way to the cloud via our Content Hub, but the concepts are rooted in history and science.

What’s in a NAM, GAM or VDM?

Anyone who has delivered to (or been interested in delivering to) Netflix is familiar with these terms: non-graded archival master (NAM), graded archival master (GAM), and video display master (VDM). Other studios or facilities may have similar assets or similar names, and these are ours. Generally speaking, each delivery to Netflix will include these archival assets.

A non-graded archival master (NAM) is a complete copy of the ungraded, yet fully conformed, final locked picture including VFX, rendered in the original working color space such as ACES or the original camera log space, with no output or display transform rendered in or applied.

A graded archival master (GAM) adds in the final color grading decisions to the fully conformed final locked picture, and is also rendered in the original working color space such as ACES or the original camera log space — again, with no output or display transform rendered in or applied.

Neither the NAM nor GAM are very pretty to watch because images in logarithmic a.k.a “log” or linear space are not rendered for display and potentially hold more information than displays can reproduce. For that, we have the VDM.

The video display master (VDM) is also a complete copy of the fully conformed, final locked picture with VFX, this time rendering in the output or display transform, meaning it is encoded in the mastering display’s color space.

NAM-GAM-VDM Example: Log workflow (ARRI LogC)
NAM-GAM-VDM Example: Linear workflow (ACES)

Each of these assets is delivered in an uncompressed or losslessly compressed format such as a 16-bit DPX, EXR, or TIFF sequence. These archival assets are among a variety that are required in addition to delivery of a SMPTE IMF (Interoperable Master Format) package, which is the mastering format used to derive all streaming assets.

These assets give us a huge amount of flexibility in the future because we’re preserving a copy of the show in the original color space. While retaining all the original information and dynamic range as it was originally shot, we can remaster shows (and remaster them more easily) while preserving the original creative intent, assuring they’ll continue to look the best they possibly can for years to come.

To understand where these terms and processes come from, we have to go back to film class.

Film History 101

Thinking more deeply about Netflix and the technology evolution pushing forward the creative technical work behind television and film, the last thing to come to mind might be physical film. But in fact, our NAM, GAM and VDM archival assets have their roots in over a hundred years of film history.

Today, more often than not, productions have largely moved to digital acquisition on camera cards and hard drives. A century ago, when the only image capture format was celluloid, the physical processes to handle it were developed and refined over the years that followed.

A motion picture film strip (Source: Wikimedia Commons)

In a physical film workflow, photography was completed on set and exposed film negatives were sent to a lab for a one light print. Multiple camera rolls were strung together into a lab roll, and dailies were created using a simple set of light values that made a positive human-viewable print.

An editor would cut together the film and a negative cut list (similar to an EDL, with a list of edit decisions and key codes instead of file names and time codes) was sent to a negative cutter for conforming the locked picture from the original negative.

This final cut glued together by a negative cutter is the equivalent of our modern day non-graded archival master (NAM).

After this point, a director of photography would work with a color timer to apply a one-light to the entire negative, then give creative adjustments on each scene. The color timer would program the printer lights on a shot by shot basis in an analog process, creating what would be similar to a modern color decision list (CDL). When the color was agreed upon, the negative was printed with the timing lights. This second negative — a negative of a negative — was called the interpositive (IP).

This IP or “negative negative” with all the final color decisions included is the equivalent of our graded archival master (GAM). Since this film stock is based on the original negative, it can hold the same amount of information and dynamic range as the original negative.

Internegatives were created from the IP for bulk printing, and a positive (human viewable) film print was created from that. A print film stock, unlike negative film stocks, has specific characteristics required to produce a pleasing image when shown on a film projector. The film print is our equivalent of a video display master (VDM).

35mm film print (Image courtesy of Adakin Productions) Source: Wikipedia

As film continued to evolve and transition into digital workflows, motion imaging experts have continued to innovate and improve this process and transition it into modern workflows. Decades of work on moving pictures combined with the proliferation of faster, cheaper storage and smaller, better camera sensors have led to the ability to create a robust archive ready for remastering where no scenes will ever be lost to time.

Next Up, Science Class: Color Science

To create these archival assets in today’s digital workflows (and maintain a happy creative team viewing their show exactly as they shot it at every point in the process), proper color management is key from the start. A basic understanding of color science is helpful in understanding how and why color, perception, and display technology is critical.

Most pictures today are color pictures. Color is made up of light, which at different wavelengths we would call “red,” “green,” “blue,” and many other names.

Source: Colour Science for Python

This light goes through two phases:

  1. Light enters the eye and tiny cells on our retina (cones) react to it.
  2. Signal travels to the back of our brain (visual cortex) to form a color perception.
Source: Wikimedia Commons

The eye-retina portion (1) is a fairly well-understood science, standardized by the CIE into three measurable “tristimulus values” known as XYZ.* These values are based on our three cone types which respond to long (L), medium (M), and short (S) wavelengths of light.

XYZ is often called colorimetry, or the measurement of color. If you can get XYZ₁ to match XYZ₂, to the average observer, the colors will match. For example, we can print a picture of an apple, using printer dyes, which measures the same XYZ values as the original apple, even though the exact spectral characteristics of the printer dyes and apple differ. This is how most color imaging systems are successful.

The cognitive portion (2) is far more complex, and involves your viewing environment, adaptation state, as well as expectations and memory. This is known as color appearance, and is also well-studied and modeled — but we’ll save that for a future blog.

For this reason, XYZ makes for a fine and proven way of calibrating displays to match. Until someone figures out how to feed content straight to your brain, displays are the only way we can view content, so understanding their characteristics and making sure they’re working as intended is important.

But before we get to the display, we have to create the images to display.

Cameras in our industry typically attempt to respond to light as close to the human visual system as possible, using color filters to emulate the three cone responses of the human eye. A perfectly designed camera would be able to record all visible colors, store them in XYZ, and perfectly store all the colors of a scene! Unfortunately, achieving this with an electronic camera system is difficult, so most cameras are not perfectly colorimetric. Still, the “emulate-the-human-eye” design criteria remains, and most cameras do a fairly good job at it.

Since cameras are not perfect, in very simple terms, they do two things:

  1. Apply a Input Transform from raw sensor RGB → XYZ colorimetry, optimizing this transform for the most important** colors found in the real world.
  2. Apply an Output Transform from XYZ → RGB for display.

Sometimes this is all done in one step. For example, when you get a JPEG out of a camera or your smartphone, these two steps have occurred, and you are looking at what is known as a “display-referred” image. In other words, the RGB values correspond to the colors that should be coming off of the display.

It is worth noting here that broadcast cameras often operate in the same manner — they apply #1 and #2 to output “display-referred” images, which can be sent directly to a display.

Shooting RAW is different. Professional cameras allow for #1 and #2 to not be applied. This means you get the raw sensor RGB values. No color transforms are applied until you process or “develop” that image.

Now, let’s say you applied color transform #1 from above, but not #2, and you output an image in XYZ. This is known as a “scene-referred” image. In other words, the values correspond to the estimated*** colors in the scene, either in XYZ or an RGB encoding defined within XYZ.

Scene-referred images usually contain much more information than displays can show, just like a film negative. This is true in both dynamic range and color. This can be stored in various ways. Camera companies in our industry usually define their own “scene-referred” color spaces.

Just a few examples include:

  • ARRI: Alexa LogC Wide Gamut
  • Sony: S-Log3 S-Gamut3.cine
  • Panasonic: V-Log V-Gamut
  • RED: RED Wide Gamut Log3G10

These color spaces are designed specifically to encompass the range of light and color that each camera is capable of capturing, and are storable in an integer encoding (usually 10-bit or 12-bit). This takes care of the Input Transform (#1).

It might seem appropriate to simply show the scene-referred color on a display, but the Output Transform (#2) portion is required to account for differences in luminance between the scene and display, as well as the change in viewing environment. For example, a picture of a sunny day is not nearly as bright as the physical sun, so this must be accounted for in terms of contrast and color. This concept of “picture rendering” has many approaches, and goes beyond the scope of this blog post, but since it has a large impact on the overall “look” of an imaging system, it is worth introducing the concept here.

For this reason, camera companies usually provide default Output Transforms (in the form of look-up tables or LUTs) so that you can take a Log image from their camera and view it in a color space such as Rec. 709.

Source: Kodak

An Eye Toward Color Management

These concepts all come together to form a color managed workflow. Because color management assures image fidelity, predictability of viewing images, and ease with mixed image sources, it’s the best way to work to protect the present and future viewing of movies or series. A color managed workflow requires a defined working color space and an agreed upon output transform or LUT, clearly documented and shared with all parties in a workflow.

Once a working color space is defined, all color corrections are made within that space. However, since we know this space is scene-referred and can’t be viewed directly, the output transform must be used to preview what the image will look like on your display.

In this example, the working color space is Log and the display color space is Rec. 709. The Output Transform separates the two, and is only baked in for the Rec. 709 streaming master. The archival masters (non-graded and graded) remain in Log color space.

It might seem easier to just convert all images to a display color space like Rec. 709. But this removes all the dynamic range and additional information from the post production process and the resulting archive. As new display technology emerges as years pass by, your images are stuck in time like Kylee’s parents’ wedding photos.

Using an Output Transform or display LUT — such as a creative LUT designed by a colorist or DI facility, or even a default camera LUT like ARRI’s 709 LUT — can not only serve as the base “look” of the show, but it protects and preserves the working color space and the full dynamic range it has to offer for color and VFX, and the eventual NAM and GAM archival assets.

Additionally, in productions with secondary cameras, an Input Transform can be used to convert images into this larger working color space. Most professional cameras have published color space definitions, and most professional color grading software implement these in their toolsets. This unifies images into a common color space and reduces time spent matching different cameras to one another.

The Academy Color Encoding Standard (ACES) is a color management system which attempts to unify these “scene-referred” color spaces into a larger, standardized one. It covers all visible colors, and uses 16-bit half-float (32 stops of linear dynamic range) encoding stored in an OpenEXR container, well beyond the range of any camera today. Camera manufacturers also publish Input Transforms in order to convert from their native sensor RGB into ACES RGB.

Source: Academy of Motion Picture Arts and Sciences

ACES also defines a standard set of Output Transforms in order to provide a standard way to view an image on a calibrated display, regardless of the camera. This part is key in terms of providing a consistent view of working images, since these ACES Output Transforms are built-in to most popular color grading and VFX software.

It’s worth noting that in the past, color transforms had to be exported into fixed LUTs (look-up tables) for performance reasons. However, increasingly with modern GPUs, systems are able to apply the pure math of a color transform without the need for LUTs.

But what about viewing it all?

Similar to camera color spaces, display color spaces are typically defined in XYZ space. However, no current displays can properly show everything in most scene-referred images due to absolute luminance and color gamut limitations, and the evolution of display technology means what you can see will change year by year.

Displays receive a signal and output light. Display standards, and calibration to those standards, allow us to send a signal and get a predictable output of light and color.

Today, most displays are additive, in that they have three “primaries” which emit red, green, and blue (RGB) light, and when combined or added, they form white. The ‘white point’ is the color that is produced when equal amounts of red, green, and blue are sent to the monitor.

Source: Wikimedia Commons

Display standards exist so that you can take an image from Display #1 and send it to Display #2 and get the same color. In other words, which red, green, blue, and white are being used?

This is especially important in broadcast TV and the internet, where images are sent to millions of displays simultaneously. Common display standards include sRGB (internet, mobile), Rec. 709 (HD broadcast), Rec. 2020 (UHD and HDR broadcast), and P3 (digital cinema and HDR).

These standards define three main components:

  • Primaries, usually defined in XYZ
  • White point, usually defined in XYZ
  • EOTF (Electro-Optical Transfer Function / signal-to-luminance (Y))

Source link