Pillow-SIMD – Fast, production-ready image resize for x86
blog.uploadcare.com
blog.uploadcare.com
http://entropymine.com/imageworsener/pixelmixing/
I implemented this for my image pipeline:
https://github.com/pedrocr/rawloader/blob/230432a403a9febb5e...
Makes for simple enough code and even before any serious effort at optimization or SIMD it can convert a 3680x2456x4 image in 32bit float (source article is 3x8bit) to 320x200x4 also in 32bit float in about 60ms (across 4 threads in 2 cores on a i5-6200U).
Edit: If you want to do even better than the tensor product Lanczos filtering, you can do a filter based on Euclidean distance. Make sure that you work in linear (non-gamma-adjusted) color space.
A couple more resources to read: http://www.imagemagick.org/Usage/filter/#cylindrical http://www.imagemagick.org/Usage/filter/nicolas/
Edit 2: really weird that this is getting downvoted. There’s not really anything to dispute here. It is straight-forward to show that the Lanczos method has objectively better output. Moreover, Alvy Ray Smith’s paper is a classic which anyone interested in image processing should read.
You have reasonable behavior along the orthogonal dimensions, but you're introducing a sqrt(2) stretch factor along diagonals.
The stretch factor along diagonals will be the same for any separable filter will it not?
My own pipe-dream is that we would use a triangular grid (if you like, the Voronoi cells here are hexagonal pixels) for intermediate image representations. This is more spatially efficient and has nicer 2-dimensionnal frequency response than a square grid, and displays are so heterogeneous nowadays that we need to do some amount of resampling for output pretty much all the time anyway, and our GPUs are getting fast enough that resampling at high quality from a hexagon grid to a square grid for output should add only relatively cheap overhead.
Pixels are often not representative of little squares (rectangles). However, they are more likely to be representative of little rectangles now than they were in 1995, now that most photographs are captured on Bayer arrays of square sensors, most computer-generated images are rendered as an average of point samples weighted according to estimated coverage of a square pixel, and everything is displayed on LCD or OLED panels with square pixels comprised of rectangular subpixels. If you take a random JPEG or PNG off the Internet, interpreting the pixel data as point samples will usually be less accurate than interpreting them as integrated over a rectangle. Interpreting the data as integrated over a gaussian distribution is also less realistic than the rectangle interpretation. Doing image processing in a point sample or gaussian context is certainly useful, but it's definitely not more fundamentally right than the little square model. Historically, that context was at best a neutral choice that was equally unrealistic no matter what kind of hardware you were working with, but mathematically convenient.
The paper's arguments about coordinate systems (whether the y-coordinate should grow toward the top of the screen or the bottom, and whether pixels should be centered on half-integer points) are also a waste of time for the modern reader.
In practice most images go through multiple layers of processing, physical sensor and display pixels come in all kinds of wacky shapes (and as you point out have channels offset by different amounts, etc.). It’s usually better to treat pixels as point samples for generic intermediate processing because you have no idea what kind of source an image comes from, and you have no idea what someone down the line is going to do with your image before sending a final signal to the display hardware. Creating synthetic images by integrating over little rectangles produces markedly inferior results to applying some real resampling filter to the samples, but gets done by computer games etc. because it’s computationally cheap, with the hope that there are enough pixels moving fast enough that someone won’t notice the artifacts.
Whether pixels are on the half-integer points is a completely arbitrary decision, unless you're trying to mix raster and vector graphics. Then the correct solution will become obvious.
[1] Most sensors are bayer patterns so some extra considerations apply to color resolution
If you want to do the correct thing from a signal processing perspective, you should upsample your images with a square-pixel filter until the Nyquist frequency is below the limit of human vision first. Then you can do your operations on the pixels as point samples before downsampling again with a square-pixel filter.
This may be true in terms of the sheer amount of GPU rasterized imagery.
I wrote the pixel filtering code currently used in one of the major production-quality film renderers. I can tell you that it uses the classical approach of treating pixels as point samples. Notionally, it convolves irregularly placed camera samples with a reconstruction filter to create a continuous function which is then point sampled uniformly along the half-integer grid to produce the rendered image.
Regarding the squarish pixels on the shown screen, I like to view them as just an analog convolution of those discrete samples so that the photoreceptors in our eyes can resample them.
You don’t have an email address in your HN profile. Is there one folks trying to contact you should prefer (before I hunt around the internet looking for one)?
And no, intentionally not patented.
The real point of the paper is that a pixel is a sample in a band-limited signal, not something that covers area. That's still just as true today, no matter what camera or display, no matter what pixel shape you're using. The point behind the paper still stands, even if the shape turns out to be a square, so we shouldn't get too hung up on the title and language railing against a square specifically.
While true that display pixels are more square today than when it was written, that's only one minor piece of the puzzle. Because we're talking about image resizing, there are multiple separate filters to consider, and for resizing it would be bad to treat pixels as squares even if you can.
If a camera's pixels are little squares, and we want to sample and then resize that image, our choice of resize filter needs to account for the little squares. We can't use a lanczos filter at all, we'd have to use something else entirely.
The big problem you have is that a sampled signal is band limited, and we treat them as perfectly band-limited. We have a body of knowledge about how to use and reason around perfectly band-limited signals, we don't have a strong image resizing theory for sampled data that is made of high-frequency samples.
If you don't convert to an ideal band-limited signal during initial sampling, then you'd have to keep the kernel shape with the image as some kind of meta data, and you'd have to use that during image resizes. If we don't have a perfectly band-limited signal, then our resize filter will always be larger than the ideal resize filter, and resizes with square pixels will take longer than resizes with band-limited point samples.
> The paper's arguments about coordinate systems are also a waste of time for the modern reader.
I'm curious why? These are still issues if you write a ray tracer, or if you mix DOM and WebGL in the same app. The paper was written for what was the SIGGRAPH going audience -- professors and Ph.D. Students -- at the time, which were all graphics researchers just learning about signal processing theory for the first time. Graphics textbooks today still cover Y-up vs Y-down for images and 0.5 offsets for pixels.
Or, in the case of fonts, they're rendered by computing the exact area coverage of the polygon over…a square representing each pixel.
If you're downsampling a line-art image, for example, you may actually be better off with some variation of the box filter. The negative lobes on the Lanczos filter can induce objectionable ringing because line-art tends to be full of what are basically step functions. It's a similar issue as with strong mosquito artifacts on JPEG compressed line-art.
A filter function that is everywhere non-negative, such as the box filter or a Gaussian can't suffer from this problem. Of the two, the box filter will give you sharper results but of course it doesn't antialias as well, either.
Faster depends on implementation details, but from the looks of it, to implement pixel mixing, you either have to generate your convolution kernel dynamically, or chop some source pixels into potentially 4 separate pieces? I would guess that it's easier to optimize a static kernel than to make a dynamic kernel faster than a static one. But I'm not entirely sure how fast pixel mixing could be made.
> It can be confused with a box filter or with linear interpolation, but it is not the same as either of them... [pixel mixing] Treats pixels as if they were little squares, which gets on some experts’ nerves.
This is a funny way of putting it. Pixel mixing is definitely using a box filter, just slightly differently than what people normally call box filter resizing. The author even says that later: "Another way to think of pixel mixing is as the integral of a nearest-neighbor function." Pixel mixing as described here is clipping the source image under the box filter rather than using a static kernel. The reason that a box filter isn't ideal is well understood. It's because the filter itself has high frequencies in it. This is the reason that the quality of pixel mixing resizes is low.
One of the benefits of a box filter is that applied multiple times, it becomes a better filter, and approximates a Gaussian after several times. I'm not sure but I would bet that clipping to the exact filter boundary actually prevents you from being able to do that with pixel mixing.
Did you call it pixel mixing in the 80s? I've been doing image resizing for decades and never heard the term before tonight. I have implemented a couple of very similar algorithms to pixel mixing for CG films, once in a shader and once for antialiasing in a particle renderer.
I worked on a major application years later that was calling it bilinear, until someone pointed out that bilinear had a completely different definition. I think we renamed it Weighted Average.
I hadn't either. I just searched for a name for the algorithm I also came up with independently. The imagemagick docs had a particularly comprehensive discussion on resizing options and linked this page with this naming for the algorithm.
Obviously, your implementation could be further optimized, but quality drawbacks will remain the same.
On the quality drawbacks I'd have to do some more checking. This algorithm is closest to what the image would be if you had a camera with that native sensor size. The standard filtering approach may very well have plenty of cases where it produces better output but it can also cause issues so I wonder if this isn't a conservative solution.
I've implemented it generally by weighing the pixels by the actual area overlapping so border pixels get weights <1.0 for non power of two reductions. Haven't seen any artifacts from it.
Resampling simulates physical properties of the light, not subjective perception of the eye.
It seems tough to come up with hard and fast rules for whether to mimic the linear physical processes, or work in a perceptual space more like the human visual system. I'd love to hear about more rigorous work in this area - most things I read have boiled down to "this way works better on these images".
It's interesting for example that using Sinc-type filters to resize truly linear data, like that from HDR cameras, usually gives rise to horrible dark haloing artifacts around small specular highlights, despite that being the most "physically correct" way to do it. Doing the same operation in a more perceptual space immediately sorts out the problem.
Second, you'll see a real profit of color management only on a few images. Most time you'll see the difference only when you see both images at the same time on the same screen.
For now, I came up to the resizing in original non-linear color space and saving the original color profile with the resulting image.
Resizing in gamma-adjusted space (sometimes) causes nasty artifacts when resizing. If you can afford the CPU use, always convert to an approximately linear space first, then downsize, then convert back. If you get the gamma curve slightly wrong (e.g. gamma = 2.0 vs. 2.2) it’s not too big a deal, the resulting artifacts won’t really be noticeable, so feel free to use a square root routine or something if it has better performance.
You might be able to do this as a 3-part process instead of expecting the resizing to handle it natively. But that brings up a good question, does the new SIMD goodness work on anything other than 8-bit data? You couldn't do linear in anything less than 16 bits.
People go all crazy about interpolation and then get the brightness wrong. It's even more obvious for high resolution photos of a tree or grass in bright sunlight. Once you start looking you notice the change in brightness everywhere, when you click on a thumbnail or while a JPEG is loading.
I am curious if the reason that Pillow-SIMD is more than 4x faster than IPP is due to features IPP supports - like higher internal resolution - that Pillow-SIMD doesn't? The reported speeds here are amazing, and I'm definitely going to check this project out and probably use it, but I'd love a little clarity on what the tradeoffs are against IPP or others. I assume there are some.
In Photoshop I always convert to 16-bit linear color before doing any kind of compositing or resampling.
About IPP's features: the comparison is pretty fair: the same input, the same algorithm and filters, pretty much the same output. If IPP uses more resources internally with the same output, so, maybe it shouldn't.
Shame on me, I still haven't added the link to IPP's test file I used. Here is it: https://gist.github.com/homm/9b35398e7e105a3c886ab1d60bf598d... It is modified ipp_resize_mt program from IPP's examples. If you have installed IPP, you'll easily find and build it.
Sadly, no, I wish I did. I just made some expensive mistakes printing giclee images from downsampled digital files, and whipped up my own dither for converting 16bit to 8bit. It wasn't until it bit me that I noticed Photoshop does it better than most apps because dithering is on by default. That's when I went looking and found an option for it in Photoshop's settings.
The main banding problem when downsampling is with slow changing gradients. Sky and interior walls, for example. I bump into it a lot with digital art too, since the source images don't have any noise. But even when there's noise in the source image, downsampling 2x or more with a good filter can eliminate the noise and cause gradients to stabilize and show their edges in 8bit color. In my experience, the problem is more common with print than on-screen resized images, but it's still pretty easy to spot on a screen, especially in the darks, and especially when jpeg compressing the results.
Implementation wise, the 16-to-8 bit dither is nowhere near as sensitive as the dithers we normally see converting 8bits to black & white or when posterizing. Almost anything you come up with will do. You don't need any fancy error diffusion or anything like that. Here's what I do: imagine the filtered 16 bit result as an 8.8 fixed point number in the [0-256) range, so the least significant bits are in the [0-1) range. I add a random number between -0.5 and +0.5 before rounding to the nearest integer. Viola, drop the low 8bits and the result is a dithered 8 bit value.
What I just described will be way slow in your world if you call a random number function every pixel, so don't do that. :P For Pillow-SIMD you'd want a random number lookup table or something slightly smarter than a random() function. And I dither on the color channels separately, but there might be some way to make it blaze by dithering the brightness and rounding all three channels up or down at the same time. I've just never tried to optimize it the way you're doing, but if you find a way and release anything that dithers, I would LOVE to hear about it.
I think I have already seen it in a couple of recent posts about image compression. (Fits perfectly the definition of Baader-Meinhof phenomenon [1].)
[1] https://en.wikipedia.org/wiki/List_of_cognitive_biases#Frequ...
Lenna does IMO not contain enough sharp edges and contrasts to highlight the differences between the different resize techniques.
With Bologna, you can clearly see the problems with a nearest-neighbour approach. I'm not sure that would have been equally visible with lenna.
Bu the way, thank you fo pointing out that this is Bologna! I'm going to Italy at the end of the month, can visit it :-)
[1] vImageScale_ARGB8888( ).
[2] I don't have identical hardware available to time on, and it's doing an alpha channel as well, so this is slightly hand-wavy.
I've built production systems over the last few years with it that really wouldnt have been possible without it.
In a 2012 vintage Nvidia article[1] they get 5-6 GB/s in both directions (array size 4MB) which would be around 1500 Mpix/s with 8bit RGBA pixels.
15 Mpix image: Transfers both ways would take 20 ms, and given GPU kernel going at ~5x the CPU speed (CPU 30, GPU 150 Mpix/s), you would spend 100 ms doing the computation. So 120 ms on GPU vs 500 ms on the CPU.
[1] https://devblogs.nvidia.com/parallelforall/how-optimize-data...
edit: so I have no idea about the real GPU spedup, but this shows that the transfers shouldn't hurt too much unless the speedup vs CPU is very small.
Also, a common use-case on the web today is to have one input image and then a large number of output images (usually smaller) for different screen resolutions & thumbnails. Seems like you could save a lot of time by uploading the input image once and then running a bunch of resize convolutions for different output sizes while it's still in the GPU memory, then download the output files as a batch.
When I wrote my own resizing code, I found it helpful to debug using a nearest-neighbor kernel: 1 from -0.5 to 0.5 and 0 everywhere else. It shook out some off-by-one errors.
Given that most cameras are producing JPEG now, I'm curious why you don't make use of the compressed / frequency-domain representation. To a novice in this area (read: me), It seems like a quick shortcut to an 8x or 4x or 2x downsample.
Or is the required iDCT operation just that much more expensive than the convolution approach?
You could probably go for another speedup, independently of DCT downscaling, by operating in YCbCr before a colorspace conversion to RGB. For example, for 4:2:0 encoded content (a majority of JPEG photographs), you end up processing 50% less pixels in the chroma planes.
When you combine both techniques, you can have your cake and eat it too: for example, to downsample 4:2:0 content by 50% you can do a DCT downscale on only the Y plane, keeping the CbCr planes as they are before colorspace conversion to RGB. No lanczos required!
If you need a downsample other than {1/n; n = 2,4,8}, you can round up to the nearest integer n then perform a lanczos to the final resolution: the resampling filter will be operating on a lot less data.
On quality I once saw a comparison roughly equating DCT downscaling to bilinear (if I can find the reference I'll update this comment). With the example above, it really depends on how you compare: if you compare to a 4:2:0 image decoded to RGB where the chroma is first pixel-doubled or bicubic-upsampled before conversion to RGB then downsampled, it might be that the above lanczos-free technique will look just as good because it didn't modify the chroma at all. Ultimately it's best to try-and-compare.
Lastly you could leverage both SIMD and multicore by processing each of the Y, Cb, and/or Cr planes in parallel.
I'd love to see vips in the benchmark comparison, perhaps a Halide-based resizer too as those are the fastest I've found so far. Perhaps GraphicsMagick too, as I believe it's meant to be faster than ImageMagick in many cases.
>> maxNumCompThreads(1);
>> im = randi(255, [2560, 1600, 3],'uint8');
>> timeit(@()imresize(im,[320,200],'bilinear','Antialiasing',false))
ans =
0.0083
ans =
0.0301
>> timeit(@()imresize(im,[320,200],'bilinear','Antialiasing',false))
ans =
0.0062
ans =
0.0113
>> maxNumCompThreads(1);
>> timeit(@()imresize(im,[320,200],'lanczos2','Antialiasing',false))
ans =
0.0146
>> timeit(@()imresize(im,[320,200],'lanczos2','Antialiasing',false))
ans =
0.0049
I take an image of 2560x1600 pixels in size and resize it to the following resolutions: 320x200, 2048x1280, and 5478x3424
I haven't done the benchmarks for a fully optimised implementation, but comparing naive implementations you can easily tell that FFT-based is much faster (even with all of the tricks that MATLAB does to optimise sparse matrix operations).
you ever consider pushing the work entirely to the client with a resize implemented in javascript? that would cut down on bandwidth as well.
even so, if the service is hosting your images in multiple resolutions you could do it all client side at upload time. they'd be trading bandwidth for cpu time.
https://blog.uploadcare.com/image-resize-in-browsers-is-brok...
It originally was written two years ago, so some things have changed. But in general, it is still correct: for most browsers, you need to combine several ugly technics to get suitable results. Though, the quality will have nothing common with quality when you have direct access to hardware.
I would much rather this feature be in Pillow so ALL of the python ecosystem could get 6 times faster image resizing.
This is devil's advocate, but did you guys have concrete need for this optimization? You now need six times fewer servers, but was that a crippling problem, or is it a cool statistic for the future when you get more users?
Even discounting that, the fact that their server bill will now be 6x smaller is justification enough? Even if the cost savings aren't quite that much (suppose they work out to be 50% of previous costs), if I was running a business I would totally be implementing optimisations that allowed me to halve my running costs...