  This paper presents a simple and efficient method to convolve an image with a
Gaussian kernel. The computation is performed in a constant number of
operations per pixel using running sums along the image rows and columns. We
investigate the error function used for kernel approximation and its relation
to the properties of the input signal. Based on natural image statistics we
propose a quadratic form kernel error function so that the output image l2
error is minimized. We apply the proposed approach to approximate the Gaussian
kernel by linear combination of constant functions. This results in very
efficient Gaussian filtering method. Our experiments show that the proposed
technique is faster than state of the art methods while preserving a similar
accuracy.
