This article will (hopefully) convince the majority of the printing industry that using the Perceptual Rendering Intent should be the default choice for converting images from one gamut (e.g. Adobe RGB) to another (e.g. FOGRA51).

First, we introduce some basics about gamut mapping. This leads to the four Rendering Intents (RI) defined by the ICC, which are briefly explained. After that, the reason for the current popularity of the non-ICC rendering intent "relative colorimetric with black point compensation" (rel.col.bpc) will be explained. The final section explains why today's gamut mapping algorithms, combined with the limitations of the ICC relative colorimetric intent, lead to the proposal to switch to perceptual as the default rendering intent for photographic content.

Output devices have limitations in terms of its capabilities to reproduce color. A simple example is a black&white printer. If a color image has to be printed on such a printer it needs to be changed in a way that it can still be recognized as that image. Methods to calculate these changes are called „Gamut Mapping“.

The adjustments that are necessary for the complete gamut can be separated into components. This is useful to understand the necessary compromises that a user has to expect.

Practical gamut mapping algorithms use a mix of chroma, lightness, and hue mapping to achieve certain tradeoffs. A typical trade-off might be that a color needs to be printed much darker to maintain its hue. Or certain colors may be printed lighter to maintain contrast between them. The compromises that are chosen depend on the intent of the user who wants to reproduce the image, or in other words, the user's "rendering intent".

The ICC defines four rendering intents to convert from one gamut to another gamut. These are:

• Perceptual
• Saturation
• Relative colorimetric
• Absolute Colorimetric

Today's printing workflows are often configured to use an extension of the ICC rendering intents originally implemented by Adobe®. It is known as Black Point Compensation or BPC. The author believes that the reason for its popularity lies in the history of gamut mapping algorithms.

Gamut mapping was implemented in imaging systems long before the ICC was created. Analog originals were scanned with drum scanners and transferred to film and later to digital data. These analog originals had a very large dynamic range that needed to be compressed to the much smaller dynamic range of a printing process. When ICC was introduced, vendors adopted these very strong compressions in the look-up tables for their profiles. However, by the time ICC technology became popular, image content was already being created using flatbed scanners and digital cameras with a more limited dynamic range. Thus, the compression in the lookup tables was too strong, resulting in a large difference between the original image and the mapped representation. As an alternative, people tried to use the relative colorimetric intent. However, since all out-of-gamut colors are clipped to the gamut boundary, the contrast, especially in the dark areas of the image, was completely lost. A solution was implemented by Adobe® called "Black Point Compensation". This method compresses the source gamut to the target gamut based on the black point differences. As a result, the original appearance of the image appears to be preserved.

Like most technologies, BPC has its limitations. It does not take care of the necessary compression in chromatic areas. As a result, it is quite possible to lose too much contrast in vivid areas of the image. Another important limitation is the ICC specification. For relative colorimetric it is defined that the gray axis must be mapped relative to the white point of the target gamut. Speaking of printing, this means to the white point of the paper. This specification makes BPC very unsatisfactory for many users with the growing number of papers with a huge amount of optical brighteners. These papers have a bluish to pinkish appearance. Many people call it "cold". When images are converted to such paper, the gray axis also becomes very bluish or "cold". This also means that there will be a huge difference between prints even if the color gamut is very similar.

Unlike relative colorimetry, the perceptual rendering intent can be freely defined by the developers of the ICC profiling engine.Thus, it is very possible to implement a gamut mapping that preserves the original image's gray axis much better than BPC is allowed to do. Also, the algorithms have evolved a lot over time and are optimized for the dynamic range of today's images (the author ignores HDR content for this article). Users should rethink existing workflows and try a perceptual rendering intent offered by modern ICC profiling engines like the MYIROtools Profiler. It preserves the original image appearance even in chromatic areas and is able to maintain a neutral gray axis even on colored substrates.

And to answer the original question:

BPC ≠ Best Possible Compromise

BPC = Bad Perceptual Cheat

In print production there are usually two conversions with gamut mapping involved. The first gamut difference between original photo and an exchange space (like FOGRA51) is usually big and users should prefer the perceptual rendering intent.

When it comes to printing this content a relative colorimetric conversion to the real printing conditions gamut is usually a good choice.
The reason for this is the fact that the gamut difference is usually small and does not need compression anymore.

In cases where the production paper has a very different white point compared to the exchange space a perceptual mapping is again superior.

What better way to prove it works than by testing with your own files?

We now have an online trial of our Neutralize OBA settings, please follow the link below to take advantage of it!