The following article is based on a journal by Jorge Jimenez, Jose J. Echevarria, Tiago Sousa and Diego Gutierrez.
You can view their SMAA demo here. It runs fine on my GTX 960 2GB.
Old Methods of Antialiasing
For years, MSAA (Multisampling Antialiasing) and SSAA (Supersampling Antialiasing) have been de facto the methods of antialiasing. In fact, these two still retain the highest quality amongst the various modern antialiasing methods. As we know, aliasing is caused by the lack of samples, in spatial level (jagged lines) and in temporal level (flickering), usually around the edges and high/low contrast regions of the picture. To battle these, we have two main ways that were once the only way around, Supersampling, and Multisampling: In Supersampling, we blow up the picture, then downsampling for the final resolution. It works fine because, as my uncle puts it, it’s a pincer attack, because it covers every basi s of the problem and surrounds it. Multisampling is similarly pincer-ish. In this method, each sample gets duplicated based on the given coefficient. In today’s high resolutions, it would require a rather fiendish graphics card to achieve this. Therefore, we need new methods of antialiasing, both in spatial, and temporal level. All these methods rely on one algorithm to do their job: edge detection algorithm. But they rely on other things as well.
Modern Ways of Antialiasing
There are many modern filter-based methods for antialiasing which all, although inferior to the former and latter, do their job. FXAA, DEAA, GPAA, GBAA, CSAA, EQAA, DLAA… In this article, we’ll talk about SMAA, and its predecessor, MLAA. These modern filter-based methods have their own problems:
- Most edge detection methods, which are the basis of these methods, only take into account numerical differences between pixels, ignoring how they appear to the viewer.
- The original shape of the object is not always preserved, an overall rounding of corners is most of the times clearly visible in text, sharp corners, and subpixel features.
- Most approaches are designed to handle horizontal or vertical patterns only, ignoring the vericals.
- Real subpixel features and subpixel motion are not properly handled. Specular and shading aliasing is not completely removed.
You’ve guessed right: We raise these issues because aim to decimate them.
Morphological Antialiasing (MLAA)
MLAA tries to estimate the coverage of the original geometry. To accurately rasterize an anitalised triangle, the coverage area for each pixel inside the triangle must be calculated to blend it properly with the background. MLAA begins the image without antialiasing, and it reverses the process by re-vectorizing the silhouettes, in order to estimate such coverage areas. Then, since the background cannot be known after rasterization, MLAA blends with a neighbor, assuming that its value is similar with the original background. In other words, The algorithm detects borders (either using color or depth information) and then finds specific patterns in these. Anti-aliasing is achieved by blending pixels in the borders intelligently. MLAA has implementations in DirectX 10 and Mono Game (XNA). Games such as Fable II use it faithfully. From the creators of MLAA, comes SMAA, or Enhanced Subpixel Morphological Antialiasing, which is the main point of our post.
Enhanced Subpixel Morphological Antialiasing (SMAA)
SMAA offers reliable edge detection, and a simple and effective way to handle sharp geometric features and diagonal lines. Besides, SMAA doesn’t change the shape of the geometry, as many other methods do.
SMAA builds on MLAA pipeline, improving or redefining at every step. In particular, the edge detection is improved by using color information with local contrast adaptation for cleaner edges. It extends the number of patterns handled for preservation of sharp geometric features and diagonals. And lastly, it shows how morphological antialiasing can be accurately combined with multisampling or supersampling and temporal reprojection.
Edge detection is vital, because undetected edges remain aliased. On the other hand, too many filtered edges can reduce the quality of the antialiased image. Different information can be used to detect he edge: Chroma, luma, depth, surface normal, or a combination of them. For four reasons, SMAA uses luma:
- Less artifacts.
- Luma is always visible.
- It can handle shading aliasing.
- And finally, it’s faster than chroma.
Have this image in mind. Here’s how edge detection works: the final calculated value is a boolean called left edge boundary. Boolean values for the top edge is similarly calculated. The formula is:
All the c values are called contrast deltas.
SMAA pattern detection allows to preserve sharp geometric features like corners, deals with diagonal and enables accurate distance searches.
Sharp Geometric Features: The re-vectorization of silhouettes in MLAA tensd to round corners. To avoid this, SMAA makes the observation that crossing the edges in contour lines have a maximum size of oen pixel, whereas for sharp corners this length will most likely be longer. Thusly, SMAA fetches two-pixel-long crossing edges instead, this allows less aggressive corners processing.
Diagonal Patterns: We introduce a novel diogonal pattern detection. It consists of the following steps:
- Search for the diagonal distance and to the left and the right of the diagonal lines.
- Fetch the crossing edges and .
- Use this input information, defining the specific diagonal patter, to access the precomputed area texture, yielding the areas and and .
If the diagonal pattern detection fails, then the orthogonal detection is triggered.
Accurate Distance Search: Key to pattern detection and classification is obtaining accurate edge distance (lengths to both end of the lien) MLAA makes extensive use of hardware interpolation to speed up this process. Hardware bilinear filtering can be used as a way of fetching and encoding up to four different values with a single memory access. This linear interpolation of two binary values (that is, bilinear) producing a single floating point value is shown as:
Where and are two binary values (either 0 or 1) and x is the interpolation value.
MLAA works with a single sample per pixel. This translates to subsampling, which makes it impossible to recover real subpixel features.
SMAA, however, works in the subpixel level. This results in:
- Local contrast
- Diagonal pattern detection
- Sharp geometric features
- Accurate searches
You can view all these in the following image, with these features compared to other methods. In fact, SMAA can produce results close to SSAA 16x.
The overhead produced by each of the solutions is negligible. In particular, local contrast adaptation is only 0.08ms, the sharp geometric features detection adn accurate distance takes 0.01ms, and diagonals processing produces an overhead of 0.12ms. In short, SMAA is rather fast, slower than SSAA and MSAA, but more fruitful, and less resource-intensive.
Well, thanks for reading the article! And thanks for the writers of the journal which I used for the majority of the article. I hope you guys have a good day, and also, go on reading scientific articles on your own. It’s simple, just head to libgen.io and search for what you like — not necessarily graphics programming. Read, read and read! Don’t watch too many Youtube tutorials, it kills your senses. I can’t stress that enough. I don’t intend to tell you what to do, these are all just suggestions. I’m currently studying Structured Computer Organization and enjoying it very, very much. I recommend it for everyone, even as bedtime reading.
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