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5.2.3.2 Colormaps for single-channel pixels ¶

As discussed in Pixel colors, color is not defined when a dataset/image contains a single value for each pixel. However, we interact with scientific datasets through monitors or printers. They allow multiple channels (independent values) per pixel and produce color with them (on monitors, this is usually with three channels: Red, Green and Blue). As a result, there is a lot of freedom in visualizing a single-channel dataset.

The mapping of single-channel values to multi-channel colors is called called a “color map”. Since more information can be put in multiple channels, this usually results in better visualizing the dynamic range of your single-channel data. In ConvertType, you can use the --colormap option to choose between different mappings of mono-channel inputs, see Invoking ConvertType. Below, we will review two of the basic color maps, please see the description of --colormap in Invoking ConvertType for the full list.

• The most basic colormap is shades of black (because of its strong contrast with white). This scheme is called Grayscale. But ultimately, the black is just one color, so with Grayscale, you are not using the full dynamic range of the three-channel monitor effectively. To help in visualization, more complex mappings can be defined.
• A slightly more complex color map can be defined when you scale the values to a range of 0 to 360, and use as it as the “Hue” term of the Hue-Saturation-Value (HSV) color space (while fixing the “Saturation” and “Value” terms). The increased usage of the monitor’s 3-channel color space is indeed better, but the resulting images can be un-”natural” to the eye.

Since grayscale is a commonly used mapping of single-valued datasets, we will continue with a closer look at how it is stored. One way to represent a gray-scale image in different color spaces is to use the same proportions of the primary colors in each pixel. This is the common way most FITS image viewers work: for each pixel, they fill all the channels with the single value. While this is necessary for displaying a dataset, there are downsides when storing/saving this type of grayscale visualization (for example, in a paper).

• Three (for RGB) or four (for CMYK) values have to be stored for every pixel, this makes the output file very heavy (in terms of bytes).
• If printing, the printing errors of each color channel can make the printed image slightly more blurred than it actually is.

To solve both these problems when storing grayscale visualization, the best way is to save a single-channel dataset into the black channel of the CMYK color space. The JPEG standard is the only common standard that accepts CMYK color space.

The JPEG and EPS standards set two sizes for the number of bits in each channel: 8-bit and 12-bit. The former is by far the most common and is what is used in ConvertType. Therefore, each channel should have values between 0 to 2^8-1=255. From this we see how each pixel in a gray-scale image is one byte (8 bits) long, in an RGB image, it is 3 bytes long and in CMYK it is 4 bytes long. But thanks to the JPEG compression algorithms, when all the pixels of one channel have the same value, that channel is compressed to one pixel. Therefore a Grayscale image and a CMYK image that has only the K-channel filled are approximately the same file size.