Show HN: Trigrad, a novel image compression with interesting results
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Re: Show HN: Trigrad, a novel image compression with interesting results
#2How does the speed compare to other compression algorithms?
Re: Show HN: Trigrad, a novel image compression with interesting results
#3How does the speed compare to other compression algorithms?
Currently compressing the example image (the one of the flowers) with 100,000 samples takes 1.5 seconds + 1.5 seconds for AForge's edge detection.
I'm sure this could be increased by a huge amount if I had a not-terrible CPU or if I did some major refactors to use the GPU.
Re: Show HN: Trigrad, a novel image compression with interesting results
#4This is very cool!
Re: Show HN: Trigrad, a novel image compression with interesting results
#5How does the speed compare to other compression algorithms?
Currently compressing the example image (the one of the flowers) with 100,000 samples takes 1.5 seconds + 1.5 seconds for AForge's edge detection. I'm sure this could be increased by a huge amount if I had a not-terrible CPU or if I did some major refactors to use the GPU.
How does it compare in quality/size vs PNG ? How about using examples where JPG are traditionally bad at, such as pictures with dark gradients leading to blocky artifacts? Would that process be more efficient there?
Re: Show HN: Trigrad, a novel image compression with interesting results
#6Earlier quoted context omitted.
Currently compressing the example image (the one of the flowers) with 100,000 samples takes 1.5 seconds + 1.5 seconds for AForge's edge detection. I'm sure this could be increased by a huge amount if I had a not-terrible CPU or if I did some major refactors to use the GPU.
How does it compare in quality/size vs PNG ? How about using examples where JPG are traditionally bad at, such as pictures with dark gradients leading to blocky artifacts? Would that process be more efficient there?
PNG is lossless, so I don't get the quality comparison.
I don't think this approach can compete with JPEG and newer transform based variants for natural photos (event at edge cases), but seems like it would be nice for lossy compression of logos/general internet pics.
Re: Show HN: Trigrad, a novel image compression with interesting results
#7How does the speed compare to other compression algorithms?
Sampling should at most take linear time in the number of pixels (ignoring the edge detection). Even less if edge detection result is represented as a sparse array.
Re: Show HN: Trigrad, a novel image compression with interesting results
#8Earlier quoted context omitted.
How does it compare in quality/size vs PNG ? How about using examples where JPG are traditionally bad at, such as pictures with dark gradients leading to blocky artifacts? Would that process be more efficient there?
PNG is lossless, so I don't get the quality comparison. I don't think this approach can compete with JPEG and newer transform based variants for natural photos (event at edge cases), but seems like it would be nice for lossy compression of logos/general internet pics.
> PNG is lossless, so I don't get the quality comparison.
The point is, is that technique in between JPEG and PNG in terms of quality/size or is it worse than JPEG altogether ?
Re: Show HN: Trigrad, a novel image compression with interesting results
#9Earlier quoted context omitted.
Currently compressing the example image (the one of the flowers) with 100,000 samples takes 1.5 seconds + 1.5 seconds for AForge's edge detection. I'm sure this could be increased by a huge amount if I had a not-terrible CPU or if I did some major refactors to use the GPU.
How does it compare in quality/size vs PNG ? How about using examples where JPG are traditionally bad at, such as pictures with dark gradients leading to blocky artifacts? Would that process be more efficient there?
It's not very comparable to PNG, since they're designed for different types of imagery. I know currently Trigrad cannot handle text at all. In regards to your other comment, it's currently definitely worse than JPEG for vector imagery.
However, it handles gradients amazingly. A full colour gradient such as [0] can be made a tenth of the size since only ~500 samples are really needed.
Re: Show HN: Trigrad, a novel image compression with interesting results
#10Garland and Heckbert had a nice algorithm for this sort of thing in their 1995 paper, "Fast Polygonal Approximation of Terrains and Height Fields." The paper is mainly devoted to height fields, obviously, but at the end they demonstrate that their algorithm is also effective at triangulating color images for Gouraud-shading as well.
I'd be curious to know how this stacks up in terms of speed and quality.
EDIT: Oh yes, and there's also "Image Compression Using Data-Dependent Triangulations" and "Survey of Techniques for Data-dependent Triangulations Approximating Color Images", both by Lehner et al., 2007. I don't mean to discourage you here, just pointing out the bar to be beaten. It's a cool idea.