the attention increase fps
SMAA is a veryefficient GPU-based MLAA implementation (DX9, DX10, DX11 and OpenGL), capable
of handling subpixel features seamlessly, and featuring an improved and
advanced pattern detection & handling mechanism.The techniquefocuses on handling each pattern in a very specific way (via look-up-tables),
in order to minimize false positives in the pattern detection. Ultimately, this
prevents antialiasing features that are not produced by jaggies, like texture
details. Furthermore, this conservative morphological approach, together with
correct subsample area estimation, allows to accurately combine MLAA with
multi/supersampling techniques.The technique focuses on handling each pattern in avery specific way (via look-up-tables), in order to minimize false positives in
the pattern detection. **Ultimately, this prevents antialiasing features that
are not produced by jaggies, like texture details**. Furthermore, this
conservative morphological approach, together with correct subsample area
estimation, allows to accurately combine MLAA with multi/supersampling
techniques. Finally, the technique has been specifically designed to clone (to
a reasonable extent) multisampling reference results.We present a new image-based, post-processingantialiasing 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.