Methodologies for Understanding and Evaluation of Image Processing Algorithms

In recent years a critical need has surfaced for the development of methodologies on the evaluation and understanding of mature image processing algorithms. Effective and efficient algorithm evaluation can be accomplished through algorithm and image modeling. Algorithm performance models can be analytical, empirical or hybrid (a combination of the two). These models can determine the best operating points of an algorithm for optimum performance, as well as predict algorithm performance on non- available image data. This provides a very convenient and cost- effective way to evaluate algorithms on a wide range of scenarios, without actually collecting the imagery that represents these scenarios. This paper demonstrates the usefulness of analytical approaches to algorithm modeling.