I mentioned earlier that the X-Trans CFA strikes a different compromise between luma and chroma detail (and noise) than Bayer. As you can see from the above figure, the X-Trans array has only 88.89% of the chroma resolution (red and blue photosites) of the Bayer array (which is traded for green photosites for increased luma resolution), and with larger gaps. This is certainly part of the problem, but it’s not the whole story. The demosaicking process is an interpolation process, whereby the colors missing from the sensor data are arrived at by an algorithm’s educated guess. With Bayer, and especially in the presence an optical low pass (AA) filter, the uncertainty of the red or blue value of a green pixel in the CFA has a specific limit. With X-Trans, this uncertainty is higher in part because the distance between same-colored pixels in certain directions is greater, up to 6 pixels (this is important for interpolating across gradients)! In other words, at small scales the color of individual pixels in in-focus areas of the final image is more guess and less fact. This kind of uncertainty (with both Bayer and X-Trans) results in a type of artifact known as “false color” at the output of the demosaicking algorithm with certain subject matter. False colors are the symptom of incorrect guesses based on the limited information contained in the raw image samples. There are various techniques for mitigating this artifact, and new methods are being researched all the time. False color suppression and chroma noise reduction can, in some implementations, be treated with the same processing step, as part of the demosaicking.

Example of false colors in an X-Trans II RAW image

It’s anyone’s guess what algorithms FujiFilm’s cameras use to demosaic and denoise images, but FujiFilm’s implementation performs similarly enough to known and documented algorithms that it’s not necessary to know the all of the details to understand where the problem lies.

Shooting at higher ISO reduces the signal to noise ratio of the image and exacerbates the problem of false colors (you may have noticed this before as colored blotches in high ISO images). In order to mitigate this, FujiFilm cameras ramp up the chroma denoising along with ISO, as do cameras from other brands. But, as mentioned earlier, with the X-Trans CFA, false colors are more of a problem than they are with Bayer, and stronger filtering is required in order to smooth them away. The problem of waxiness arises because Fuji decided to use much stronger chroma NR than is strictly necessary to suppress false colors in the general case. The result is that colors bleed together (especially red/blue hues). The effective color resolution is lower than it should be, even taking that 88.89% figure into account. Teeth and eyes become the color of the surrounding skin. Rosy cheeks appear wan and corpse-like, and, generally speaking, people are rendered cartoonishly.

Contrary to popular belief, the “NR” setting in the camera’s menus does not significantly affect this chroma smoothing at all — It only impacts luminance noise reduction.

Some viewers are reported to actually prefer this effect, but technically it is an objective and measurable flaw — the rendered image is no longer representative of the scene (known as the “ground truth” in academia).

The issue becomes a significant obstacle because FujiFilm cameras give the user no control in the matter. There is no “High ISO NR” menu setting like cameras from other manufacturers have. There is no separate chroma NR setting. You either live with the reduced quality of the JPEGs or you don’t.

The alternative is to shoot RAW. Which is fine, but demosaicking X-Trans files is less efficient than demosaicking Bayer files —and, as anyone who has tried it knows, this translates to a much slower workflow — and you lose all of that “color science” too, because, sadly (shamefully, in my opinion), FujiFilm does not publish color profiles for their sensors nor embed the color matrices in the RAW files as some other manufacturers do. And if you want to use the camera’s WiFi feature to transfer the images to your smartphone and process/post them on the go— you can only do that with the JPEGs.

Examples

Because the problem is one of color resolution, you are unlikely to notice the Waxy Skin-Tones problem in headshots or the like without involving high ISOs. In those cases there are hundreds pixels whose common values overwhelm the noise and allow the color of the ground truth to show in the final image.

The problem appears in fine detail, and is particularly noticeable in human faces at a distance, but also in headshots in the capillaries in the eye or the color of fine hair (where it differs from skin tone).

I have come across several real-world instances of this problem and have decided to present these rather than some kind of laboratory setup to illustrate that it is indeed a problem encountered in practice and to make it perfectly clear what information — what objective quality — Fuji’s JPEG engine tosses out as if it were noise. Whether you care about this discarded information or not is up to you.

The following examples are heavy crops. The images are cropped to illustrate the differences for viewers with all sizes of displays. Keep in mind that FujiFilm sold us a new 24 megapixel sensor on the promise of being able to crop more. Once you’ve noticed the effect, I think you’ll be able to spot it in uncropped images too.

All examples were shot on Fuji’s new flagship camera, the X-T2 with the Fujinon 35mm F2 WR lens.

A note about FujiFilm’s JPEG output: “FINE” quality in camera translates, in more specific terms, to 99% quality level JPEG with sampling factors 2x1,1x1,1x1. (This means that there is less color resolution in the horizontal plane than the vertical, but don’t get hung up on that because much of the color information in these images is interpolated by the demosaicking process anyway and it can be shown that this level of chroma subsampling of the JPEG image cannot account for the waxy skin tone effect [this will be left as an exercise for the reader.])

Example 1

The image was shot at ISO 1600.

Fuji JPEG