On Random Testing of Image Processing Applications

Abstract

Testing image processing applications is a non-trivial task. Complex inputs have to be generated and complex test results have to be evaluated. In the present paper, models for random generation of images are proposed and compared. The study for their comparison uses mutants of one particular implementation of an image processing operator, namely an implementation of the Euclidean distance transform. Metamorphic relations, necessary properties, and special values are furthermore identified for this distance transform to enable automatic evaluation of test results. These criteria are also compared using mutation analysis. Based on the results, general hints are given on how to choose random models and automatically evaluate test results for testing in the field of image processing