Perfect window memoization: A theoretical model of an optimization technique for image processing algorithms

This work presents Perfect Window Memoization; a high-level processing model that gives an estimation (upper-bound) of performance gain for an image processed in software or hardware, obtained by eliminating the computational redundancy of the image. We show mathematically, supported by experimental data, that the computational redundancy of an image is, in fact, inherited from two basic data redundancies of the image; coding and interpixel redundancy. This is a simple, yet a revealing concept to use in practice by which images can be categorized based on their potential performance gain in software and hardware by only their fundamental redundancies, with no need to implement a mechanism to actually exploit the computational redundancy in software or hardware. The proposed model can be used as a useful tool in analyzing images from the performance perspective in the early stages of designing an optimization technique.