SMAA: Enhanced Subpixel Morphological Antialiasing
¹Universidad de Zaragoza ²Crytek GmbH
We present a new image-based, post-processing antialiasing technique, which offers practical solutions to the common, open problems of existing filter-based real-time antialiasing algorithms. Some of the new features include local contrast analysis for more reliable edge detection, and a simple and effective way to handle sharp geometric features and diagonal lines. This, along with our accelerated and accurate pattern classification allows for a better reconstruction of silhouettes. Our method shows for the first time how to combine morphological antialiasing (MLAA) with additional multi/supersampling strategies (MSAA, SSAA) for accurate subpixel features, and how to couple it with temporal reprojection; always preserving the sharpness of the image. All these solutions combine synergies making for a very robust technique, yielding results of better overall quality than previous approaches while more closely converging to MSAA/SSAA references but maintaining extremely fast execution times. Additionally, we propose different presets to better fit the available resources or particular needs of each scenario.
We thank the anonymous reviewers and the members of the Graphics and Imaging Lab for their valuable comments and suggestions. We also thank Stephen Hill, Jean-Francois StAmour, Johan Andersson, Alex Fry, Naty Hoffman, Carsten Wenzel, Nick Kasyan, Pierre-Yves Donzallaz and Michael Kopietz for their help and support. This research has been funded by the European Commission, Seventh Framework Programme, through the projects GOLEM (Marie Curie IAPP, grant agreement no.: 251415) and VERVE (Information and Communication Technologies, grant agreement no.: 288914), and by the Spanish Ministry of Science and Technology (TIN2010-21543). Jorge Jimenez is also funded by a grant from the Gobierno de Aragón.