Multi-sample stereo renderer for GPU anti-aliasing
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Solution Overview
Problem
Current anti-aliasing techniques, such as supersample anti-aliasing (SSAA) and multi-sample anti-aliasing (MSAA), face challenges in balancing image quality and computational efficiency, particularly in graphics processing units (GPUs), where they either increase computation significantly or compromise on image resolution.
Innovation Solution
A multi-sample stereo renderer is developed, which selects different sample patterns for left and right eye display frames, allowing for reduced sample sets in areas with less visual depth to improve processing bandwidth and reduce power consumption while maintaining image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If supersample anti-aliasing (SSAA) is used to reduce aliasing artifacts, then image quality is improved, but computation time and processing power increase significantly
Solution Approach 1:
The invention divides the pixel processing into segments by applying anti-aliasing only to specific pixel types (edge pixels) rather than all pixels. The rasterizer identifies edge pixels versus non-edge pixels, and only edge pixels undergo multi-sample anti-aliasing processing, while non-edge pixels use standard rendering, thus reducing overall computation time while maintaining image quality where it matters most.
Solution Approach 2:
The invention applies different processing quality levels to different regions of the image. Edge pixels receive high-quality multi-sample anti-aliasing treatment, while non-edge pixels receive standard processing. This local differentiation ensures that computational resources are focused on areas where aliasing artifacts are most visible and problematic, improving overall image quality without the full computational cost of applying SSAA uniformly across the entire image.
2Manufacturing precision
If multi-sample anti-aliasing (MSAA) is used to reduce aliasing artifacts, then image quality is improved, but the number of computations and processing operations increase
Solution Approach 1:
The rasterizer segments pixels into edge pixels and non-edge pixels based on geometric primitive boundaries. Only edge pixels are processed with MSAA techniques involving multiple sample points, while non-edge pixels are rendered using standard single-sample methods. This segmentation reduces the total number of computations required while preserving image quality at critical edge regions where aliasing occurs.
Solution Approach 2:
Instead of applying full MSAA processing to all pixels (excessive action), the invention applies MSAA only partially to edge pixels where it is most needed. This partial application maintains sufficient image quality by addressing aliasing at edges while avoiding the computational overhead of applying the same processing to all pixels, thus improving processing efficiency.
3Manufacturing precision
If full sample patterns are used for both left and right eye frames in stereo rendering, then image quality is maintained, but power consumption and processing bandwidth increase
Solution Approach 1:
The invention segments the sample pattern application in stereo rendering by identifying which pixels are edge pixels versus non-edge pixels for each eye frame. Only edge pixels receive full multi-sample anti-aliasing treatment, while non-edge pixels use reduced sampling. This segmentation reduces the total number of samples processed across both left and right eye frames, decreasing power consumption and processing bandwidth requirements while maintaining image quality at critical edge regions.
Solution Approach 2:
The invention applies partial sampling to non-edge pixels in stereo rendering instead of full sample patterns. This partial action reduces the computational load and power consumption for rendering both eye frames while maintaining sufficient image quality, as full anti-aliasing sampling is applied only where geometric edges require it, not uniformly across the entire stereo image pair.
Data Source
AI summary
An embodiment of a parallel processor apparatus may include a sample pattern selector to select a sample pattern for a pixel, and a sample pattern subset selector communicatively coupled to the sample pattern selector to select a first subset of the sample pattern for the pixel corresponding to a left eye display frame and to select a second subset of the sample pattern for the pixel corresponding to a right eye display frame, wherein the second subset is different from the first subset. Other embodiments are disclosed and claimed.