static image filtering on commodity graphics processors
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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

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filters from computer vision
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
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



filtering on a cpu vs a gpu
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
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 algorithm
Proposal for the GPU Algorithm

1. 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 a

certain offset. The blending is

a single add.


5. return pb3

the ups and downs
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
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
  • 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