This documentation page on #Blender's internal mesh data structure is really good: https://developer.blender.org/docs/features/objects/mesh/bmesh/ It has very thoughtful comparisons to half-edges.

I stumbled upon it when researching mesh libraries for Python that support N-Gons. Perhaps I'll try reimplementing BMesh myself.

Measured a 256-shade greyscale ramp through my capture card with both on my laptop and the Nintendo 64 (via Retrotink). The response is linear in both (phew!) but I had to mess with the capture card brightness & contrast settings to make the N64 closer to the input. Still clips the whites too early.

#n64 #n64dev

Today I was scratching my head why k-means didn't seem to reduce Mean Squared Error. The clustering seemed fine. Is the error computation broken? How can it be when it's so simple:

def compute_mse(a, b):
return np.mean((a-b)**2)

Well, arguments 'a' and 'b' were images with 8 bits per channel, so...

#numpy

Another example on how mean squared error (MSE) doesn't predict final image quality. I implemented Variance Cut Color Quantization in two ways, left is with greedy split plane optimization and on the right side it's with a few rounds of global k-means that results in lower MSE.

I actually prefer the image on the left side since its more detailed.

I tried Incremental Online K-Means (IOKM) for color quantization based on a 2022 paper but seems like it also adds random dithering as a side effect so it's not really useful for compressing textures.

The paper: https://web.archive.org/web/20240921154745/https://faculty.uca.edu/ecelebi/documents/ESWA_2022.pdf

Left: libimagequant, Right: IOKM.