Gaussian smoothing of sparse spatial distributions as applied to informational difference

The characterization of separation between object spectral distributions by the use of any divergence-evolved method, such as Informational Difference is problematic due to the relative sparsity of said distributions. The existence of zero-probability points renders the calculation result irrelevant as the separation is either infinite or undefined. A method to surmount this problem using available experimental data is proposed. We consider the statistical nature of measurement for all available visual data, e.g. pixel values, and model the spectral distributions of these pixels as a congregate of Gaussian statistic measurements. The inherent nature of Gaussian distributions smoothes over the zero-probability points of the original discrete distribution, solving the divergence problem. The parameters of the Gaussian smoothing are experimentally determined.