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Comment by BubRoss | Hacker News Reader

I can tell you right now that you will have a very difficult time beating a normalized gauss filter with a diameter of around 2.2 pixels in a general case.

Color and luminance is a separate and orthogonal issue from filtering. I also know that people get away with compositing without converting to linear space, but I'm skeptical that any benefits they see aren't just a matter of getting the color curve they want for free, as opposed to doing a similar correction after something has been composited correctly.

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I was working at PDI Dreamworks during one of the semi annual investigations into which filter kernel was best. And I was completely blown away by how quickly the lighting sup could identify and react to various filters. Gaussian was voted down reliably and repeatedly for being too blurry.

Personally, I like the extra blur I get (and extra safety and guarantees) you get with Gaussian. Back in the NTSC days I discovered that vertically blurring interlaced video made it noticeably more clear and visible, even though it was softer.

But, If you do really spend time with sharper filters, it is true that Gaussian is softer and some pros really do want sharper images than Gaussian can provide.

Were all the filters being compared with the same width? At the same width it will be softer, at a smaller pixel radius is where you can compare aliasing with the same visual sharpness as something like catmull-rom.

There were a variety of widths, it was one axis of the study. But I don’t remember the details, it is certainly possible you’re talking about something we didn’t test. Personally, at first I couldn’t even see the differences they were discussing.

Having studied graphics and signal processing for a few years in graduate school before that job, I thought I would be good at seeing the differences, and I was a bit shocked how good they were at it, and how not that good I was. :)

Truncating the Gaussian too closely though, and it’s not exactly a Gaussian anymore, you lose the best antialiasing properties. I can totally see how it will be sharper and more comparable to other popular filters. (Normalized & truncated at 1.1 radius is just slightly outside the 1 std dev line, right?)

Gaussian is my personal choice for large format prints of images with extreme aliasing problems.

My experience has likewise been that film DP’s have extremely impressive visual acuity, memory for color, etc. Talented artists often have developed whole sets of skills that the rest of us are unaware are even skills. In the same way a programmer might have thought about cache line false sharing as it affects memory hierarchy throughput, visual art often hides lots of expertise you cannot directly perceive, even as you can sense the quality of the whole.

I like to describe “learning to draw” as “installing a 3d modeling and rendering package on your brain, along with a decent collection of base models to modify”. It’s a complex skill set. If you start animating you get to add in a physics simulation. And you become conscious of so many little things that the layman only notices when you get it wrong.

If you want to see really careful observation skills, try dye-transfer printers like http://ctein.com/

The width of the filter is separate from how the gauss curve is used as a filter (and not everyone does it the same).

Using only one standard deviation cuts off a huge amount of the curve, ideally it would be cut off around the third standard deviation and normalized so that the value after the last would be zero.

> Having studied graphics and signal processing for a few years in graduate school before that job, I thought I would be good at seeing the differences, and I was a bit shocked how good they were at it, and how not that good I was. :)

IMO the best place to learn to see these distinctions is in careful photo printing (of the type where you spend >30m per image, and where “printing” here is being used in an old-school sense of “all of the manual steps to take a raw image from the camera and turn it into printed output”).

Spend a few months doing that for a few hours per week and your ability to see artifacts, textural details, fine differences in amount of edge contrast, etc. will shoot up. (Obviously the folks who spend 30 years on this are even better.)

Studying signal processing, optics, psychophysics, etc. is also useful for understanding what you are noticing, but it isn’t seeing practice.

Reeeaallly? I haven't made the images, but my intuition is telling me that text will be noticeably blurry compared with a box filter.

I basically agree with your points regarding compositing in a linear space, except that I suspect that thin black-on-white lines will come out looking thin and spindly.

It sounds like you are talking about text that doesn't move.

If you want a general filter that can give a result without visible aliasing while sacrificing as little sharpness as possible, a 2.2 gauss filter is very hard to beat and I have spent a lot of time trying.

Box filters can be sharper, lancoz filters can be better for scaling down a final image, etc. but they will alias in a general sense. You might not see it in static text which is fine.

Also thin black on white lines are an extreme outlier, since what you percieve is relative and the entire image matters. It is more into the realm of optical illusion that play off our relative sensitivity.

So obviously text is one of the things I care about a lot. I'm willing to accept your filter as being very good for a lot of stuff other than presenting a GUI.

Does this mean that there is not one true right answer for images, as there is for audio[0]? That you should use different reconstruction filters depending on the application. Is it accurate to say that a pixel is not a little square unless you're rendering text, in which case it is?

[0] I know this is a slight oversimplification, if you care deeply about latency you might choose between linear and minimal phase, etc. But for consumer applications it's true enough.

A Gaussian kernel will reduce ringing artifacts, but is much blurrier than alternative resampling kernels. https://www.imagemagick.org/Usage/filter/nicolas/

Does “diameter” here mean “twice the standard deviation”?

No, diameter means the width of the filter in pixels. Each side would extend 1.1 pixels for a 2.2 diameter filter.

If you compare filters at the same width, gauss will be softer, which is why you can lower the diameter.

A Gaussian filter has infinite support, and is characterized by its variance (or standard deviation). https://en.wikipedia.org/wiki/Gaussian_filter

A ‘Gaussian’ which extends 1.1 units is a non-standard and not clearly defined thing. Are you picking some fraction of 1.1 as the standard deviation and truncating after? (Often Gaussian filters are truncated after 3 standard deviations, or similar.) Are you multiplying by some other window function? ...

Can you link to a more explicit formal description of what you mean?

When I say 2.2 pixels as the diameter, I mean roughly that a pixel will be made up of all the samples 1.1 pixels or closer to the center of the pixel and they will be weighted by a gauss curve. How that gauss curve is actually used is not formal or strictly defined, but is usually a curve that goes out to about 3 standard deviation a baked into a LUT and normalized.

A curve that uses more standard deviations would have to be wider in pixels to look similar.

Do you have some code for what you specifically are talking about? Or a formal description?

You have been saying here “this kernel is better than all of the alternatives” but it’s hard to evaluate that kind of claim without knowing precisely what you mean.

“not formal or strictly defined” is not super encouraging.

I never said it was better than all alternatives, I said my experience is that it is difficult to beat in a general case.

Bake a gauss curve out to three standard deviations into a LUT and normalize it. You can look at what PBRT does.

Keep in mind that I was replying to someone confused by all the choice of filters and was giving him a very solid starting point. This isn't some grandiose claim of scientific exploration, it's experience.

True, but how often do you care about the general case? Most images are representations of real objects, or symbols or diagrams designed for human comprehension. Ignoring cases that in practice are very unlikely to occur lets you optimize for the more common cases. Techniques like resizing in a sigmoidal color space (see http://www.imagemagick.org/Usage/resize/#resize_sigmoidal ) don't have any rigorous mathematical basis, but they're tuned subjectively to have good results in common cases.

Lanczos interpolation seems to beat a gauss filter for me. It has some overshooting/ringing effects of course, but the improved sharpness is a great tradeoff compared to the blurriness of gauss filtering. Of course, gauss filtering might still play a residual role in analog systems, such as the one that physically displays stuff via a CRT screen.

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