The following are the properties of unweighted area sampling technique of anti-aliasing:
Uniform Sample Contribution:
In unweighted area sampling, each sub-pixel sample within a pixel contributes equally to the final pixel color. The coverage of each sub-pixel by the rendered shape is not weighted differently based on its position within the pixel.
Sub-Pixel Grid Division:
The pixel is divided into a uniform grid of sub-pixels. Common divisions might be 2x2, 4x4, or 8x8 grids, where each sub-pixel is treated independently.
Binary Coverage Evaluation:
Each sub-pixel is evaluated in a binary manner: it is either covered by the geometric shape or not. The final pixel color is determined by the ratio of covered sub-pixels to the total number of sub-pixels.
Simple Averaging:
The final pixel color is obtained by averaging the colors of all sub-pixels within the pixel. Since no weights are applied, this is a straightforward arithmetic mean.
Implementation Simplicity:
The unweighted area sampling technique is relatively simple to implement compared to weighted methods or other complex anti-aliasing techniques. It does not require computing weights or gradients.
Reduction of Aliasing Artifacts:
By averaging the contributions of multiple sub-pixels, unweighted area sampling reduces the appearance of jagged edges (aliasing), providing a smoother overall image.
Increased Computational Overhead:
Although simpler than weighted methods, unweighted area sampling still requires additional computations compared to no anti-aliasing, as multiple samples per pixel need to be evaluated and averaged.
Memory Usage:
Similar to other supersampling techniques, unweighted area sampling requires more memory to store the intermediate results for sub-pixels.