Generally, the designs of digital image processing algorithms and image gathering devices remain separate.
Consequently, the performance of digital image processing algorithms is evaluated without taking into account
the artifacts introduced into the process by the image gathering process. However, experiments show that the
image gathering process profoundly impacts the performance of digital image processing and the quality of the
resulting images. Huck et al. proposed one definitive theoretic analysis of visual communication channels, where
the different parts, such as image gathering, processing, and display, are assessed in an integrated manner using
Shannon's information theory. In this paper, we perform an end-to-end information theory based system analysis
to assess edge detection methods. We evaluate the performance of the different algorithms as a function of the
characteristics of the scene, and the parameters, such as sampling, additive noise etc., that define the image
gathering system. The edge detection algorithm is regarded to have high performance only if the information
rate from the scene to the edge approaches the maximum possible. This goal can be achieved only by jointly
optimizing all processes. People generally use subjective judgment to compare different edge detection methods.
There is not a common tool that can be used to evaluate the performance of the different algorithms, and to
give people a guide for selecting the best algorithm for a given system or scene. Our information-theoretic
assessment becomes this new tool to which allows us to compare the different edge detection operators in a