Black

There are many ways to convert from color to black and white in PWP. Available transformations include tint, bit-depth conversion, monochrome, channel extraction, and desaturation, to name a few. Den has addressed some of the issues involved in B&W conversion. The purpose here is to consider an alternative method for preparing a specific type of color image for conversion.

Rationale --With color images, we visually distinguish different objects and areas of an image by differences in hue, saturation, tonal level (brightness), and sharpness. With B&W images, however, we have only tonal level and sharpness to guide our perception of an image. Except in special cases, we tend to refrain from changing sharpness during conversion from color to B&W, so definition, isolation, and emphasis of objects and areas must be based on localized adjustments to tonal levels. When an object has more or less the same tonal level as its surroundings, removal of its distinguishing hue and saturation makes for a flat image - the object tends to disappear into its surroundings.

The tendency for any particular image to behave this way upon B&W conversion can be judged from its HSV brightness histogram. Consider the following two histograms:

Figure 1. Balanced tone distribution

Figure 2. Unbalanced tone distribution

For Figure 1, the tones are spread across the available range, and conversion by any method will yield reasonably good contrast between objects and areas in the image. On the other hand, while the image levels in Figure 2 span the full dynamic range, most of the pixels are grouped in the upper end of the tonal range with the distribution tailing off through the midtones. As shown below, conversion of that image will tend to yield a relatively flat (low contrast) black and white product.

Application -- One solution to getting a better B&W product from the photo used for Figure 2 is to apply PWP's color correction transformation to selectively adjust the brightness of different hues within the image before B&W conversion. The objective is to separate the group of tones near the upper end of the histogram to achieve greater localized contrast between selected objects and areas in the converted image. The procedure is quite simple. I like to start with extraction of the HSV-V channel to help me visualize the changes that are needed. Then, open the color image in the CC transformation and sequentially select and either lighten or darken colors as needed. The procedure should result in noticeable separation and spreading of components of the histogram. Finally, convert to B&W using your favorite transformation (monochrome, channel extraction, tint, desaturation, etc.). Lastly, the contrast can be further tweaked, if necessary, using the brightness curve.

Example -- The beautiful photo shown in Figure 3 was created by Terri French, and is used as an example with her permission. A simple direct conversion obtained by extraction the HSV-V channel

Figure 3. Moab (by permission of Terri French)

Figure 4. Moab B&W HSV-V channel

is given in Figure 4. As expected from the histogram (Figure 2), Figure 4 is flat with no tonal distinction between the sky and the foreground and only minor distinction between the vegetation and rocks. Other conversions, such as changing the bit-depth can result in even muddier results.

For this case, I wanted to make the sky and the vegetation distinctly different tones from those of the foreground and rock formations. I also wanted to retain the shadow structure. Using the CC transformation, I selected the blue sky and lowered its brightness by about 70 percent. Without closing or applying the transformation, I selected the green vegetation and lowered its brightness by about 50 percent. [At this stage, the amounts are not critical; the objective is to separate the tonal values of the selected objects so that they can easily be detected for the next steps.] I then extracted the V channel from the modified color image, and opened the brightness transformation in the double histogram (input/output) mode.

The upper histogram in Figure 5 is that of the color correction derived image, while the lower histogram is for my contrast enhanced final product. Control points for this transformation were selected to that the upper end of the vegetation tones was pinned (the central vertical control point). This allowed independent expansion of both the upper and lower ends of the histogram.

Figure 6 shows the final product. Of course, others will process according to their tastes.