- The topic of aliasing and moiré effects in digital image processing is complex. In brief, and perhaps unsatisfyingly so, anytime you digitize at a specific resolution, or "down-sample" from a higher resolution to lower resolution, it is possible to introduce patterns of imaging that suggest lines or curves that are not present in the original or source image.
- Here is one article that describes the effect, both in terms of audio sampling (where I first encountered this) and in video sampling: https://matthews.sites.wfu.edu/misc/DigPhotog/alias/index.html. (That is from a physics professor at Wake-Forest U., in North Carolina. An interesting site, and not a bad explanation of what is happening).
- There is another article that correlates the frequency domain analysis and the various discrete filtering techniques in image processing, suggesting further that the gaussian filter used in EWA is an important if not perfect mechanism to reducing aliasing and moire patterns. I can’t claim to fully understand this, but it does appear to again correlate the image processing world with our reprojection efforts. https://www.strollswithmydog.com/downsizing-algorithms-effects-on-resolution/#more-954.
- Note that wherever the reprojection mapping of source data to target projection results in “downsampling” of an input area to an output area - aliasing can occur if some kind of interpolation filtering is not applied. This is not uniform across the output dataset, as reprojection itself is not uniform, but very often applies in some area of the output: https://www.strollswithmydog.com/downsizing-algorithms-effects-on-resolution/#more-954.
- I’m reminded that HEG chooses a higher output resolution that minimizes downsampling effects (eliminates?), while GDAL chooses an output resolution that preserves overall grid sizing, generally at a lesser resolution, and that can introduce downsampling artifacts. While HEG's approach may not eliminate the issue (TBD), it certainly does seems to minimize the issue somewhat.
- Subjectively I'll note that aliasing and moiré effects have been very visible in previous reprojection efforts. In the development of the Swath-Projector application both forward (EWA) and reverse (nearest-neighbor, bi-linear) projection/interpolation options were implemented. Very often the nearest-neighbor and bi-linear (without filters) introduced these effects, along with a kind of "fingers" of data along the edges, which I think is a similar result. The end results was that EWA "looked" much better. Even the Elliptically-Weighted-Nearest-Neighbor option, which uses the elliptical weights to pick the highest-weighted as the nearest value - without averaging the source data - was a significant improvement.
- I believe in the image processing world, there is a trade-off of "sharpness" and full resolution access vs. the introduction of these effects. I.e., a kind of minor "blurring" of the image is introduced, but I don't think in practice this was very significant. I suspect the gaussian filter's reduction of aliasing effects far outweigh any loss of "sharpness" in the output data.
- The "Stalls with my dog" link above describes a metric of processing shown in the frequency domain on something called the MTF chart. The ideal has the filter following source data up to something called the "nyquist frequency" and then sharply dropping off above that frequency. It can be seen that e.g., Nearest-Neighbor interpolation without filtering does quite poorly on this metric, while the gaussian filter used in the EWA technique does quite well.
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