static image filtering on commodity graphics processors

Static Image Filtering on Commodity Graphics Processors

Peter Djeu

May 1, 2003

Filters from Computer Vision

• Mean (a.k.a. average) filter– each element in a neighborhood is given equal weight;

a simple image smoother

• Gaussian– a neighborhood is weighted by a 2-D Gaussian, with

the peak at the center; a better image smoother

• Laplacian of Gaussian– The Gaussian filter is applied, and then the Laplacian

(spatial derivative is applied); good for edge detection

The Convolution Kernel

• We want to transmit pixel information from neighbors to a central pixel

• Use the convolution kernel as a window to frame the work that needs to be done

16 26 16

26 41 26

16 26 16

1 161

Filtering on a CPU vs. a GPU

• CPU– sequential and straightforward

• GPU– not so straightforward if the goal is to exploit

parallelism and maintain good locality– a pixel’s output value depends on the weighted

value of it’s neighbors, so there is dependency across various elements

Pixel Buffers in GPUS

• GPU’s do not have indirect addressing to memory, so results have to be stored in pixel buffers. The card is really rendering to an off-screen frame (writing).

• The GPU can then treat the Pixel Buffer as a texture for rendering (reading).

Proposal for the GPU Algorithm1. Store original into pb1.2. For each element ki in the convolution kernel {3. Copy pb1 into pb2, scaling by ki

in the process (use Cg shader).

4. Based on the location of ki,render pb2 into pb3 with acertain offset. The blending isa single add.

}5. return pb3

The Ups and Downs

• This technique may be fast because…– parallelism is completely possible during the scaling

stage and the blending– since most convolution kernels have symmetry, a little

bit of preprocess could mean

• On the other hand…– as image size grows, cache misses may become more

prominent, since we manipulate the whole image– when translating, coords. are interpolated, not mapped

• Tiling? Can a good size be determined in exp.?

Current Progress

• P-Buffer’s are frustrating– wglReleasePbufferDCARB() returning type

PFNWGLRELEASEPBUFFERDCARBPROC

• Lot’s of low level implementation / debugging, very much on a hardware level

• (Naïve) CPU implementation is complete and working, and P-Buffers are almost done

Results (in real-time sec’s)CPU, Gaussian Filter, w/ RGB, 24 bit targa’s

x y 5 x 5 11 x 11 31 x 31

Quake 256 256 0.4 1.5 10.5

Fruit 512 480 1.4 5.4 41.7

Ruins03 735 485 2.0 7.8 59.7

Time (s) versus Kernel Size (elts)

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Quake

Fruit

Ruins03

Time(s) versus Image Size (x*y)using a (31 x 31) kernel

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0 50000 100000 150000 200000 250000 300000 350000 400000

Applications?

• Super fast filtering techniques on 2-D images may provide tools or insight for traditionally more complex problems involving 2-D images, like categorization / classification