Full reference image quality algorithms are standard tools in digital image processing but have not been utilized for printed images due to a "correspondence gap" between the digital domain (a reference) and physical domain (printed sample). In this work, the authors propose a framework for applying full reference image quality algorithms to printed images. The framework consists of accurate scanning of printed samples and automatic registration and descreening procedures which bring the scans in correspondence with their digital originals. The authors complete the framework by incorporating state-of-the-art full reference algorithms to it. Using data from comprehensive psychometrical experiments of subjective quality experience, the authors benchmark the state-of-the-art methods and point out similar results in the digital domain: the best digital full reference measures, such as the recently introduced visual information fidelity algorithm, perform best also for printed media.