Now that we know how to create a 3×3 linear matrix to convert white balanced and demosaiced raw data into connection space – and where to obtain the 3×3 linear matrix to then convert it to a standard output color space like sRGB – we can take a closer look at the matrices and apply them to a real world capture chosen for its wide range of chromaticities.
Most of the photographs captured these days end up being viewed on a display of some sort, with at best 4K (4096×2160) but often no better than HD resolution (1920×1080). Since the cameras that capture them have typically several times that number of pixels, 6000×4000 being fairly normal today, most images need to be substantially downsized for viewing, even allowing for some cropping. Resizing algorithms built into browsers or generic image viewers tend to favor expediency over quality, so it behooves the IQ conscious photographer to manage the process, choosing the best image size and downsampling algorithm for the intended file and display medium.
When downsizing the objective is to maximize the original spatial resolution retained while minimizing the possibility of aliasing and moirè. In this article we will take a closer look at some common downsizing algorithms and their effect on spatial resolution information in the frequency domain.