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 16 26 41 1 161 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 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 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 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 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)

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

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