In the context of business process intelligence, along with the need to extract a process model from a log, there is also the need to measure the quality of the extracted process model. Hence, process model quality notions and metrics are required. We present a systematic approach for developing quality metrics for block structured process models, which offer less expressive power than Petri-nets but have easier semantics. The metrics are based on tagging an initial block structured process model with self-loop and optional markings in order to explain all the instances in the given log. Then we transform the marked model to an equivalent maximal model by rewriting the self-loop and optional markings for consistency, and determine a badness score for it, which determines quality. Our approach is compared with related work, and a plan for testing and validation on noise-free and noisy data is discussed.