Tile-Based Image Rendering With Edge Shading and Neural Super-Sampling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing tile-based image rendering methods require waiting for surrounding frames to be completed for super-sampling processing, leading to lengthy processing times and increased resource consumption.
Innovation Solution
A method that divides an input frame into tile frames, performs shading on edge regions using a shader module, and performs neural network-based super-sampling on non-edge regions without relying on surrounding frames, thereby reducing processing time and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If waiting for surrounding frames to be completed before performing super-sampling processing, then the quality of super-sampling is improved, but the processing time is increased
Solution Approach 1:
The patent divides the frame into multiple independent tile regions and processes each tile independently without waiting for surrounding tiles to be completed. This segmentation allows parallel processing of different tile regions, significantly reducing the overall processing time while maintaining acceptable super-sampling quality through local context sufficiency.
Solution Approach 2:
The patent performs shading and other preliminary rendering operations on tiles before super-sampling is needed. By preparing tile data in advance and organizing it in a format suitable for super-sampling, the system can execute super-sampling operations immediately when resources are available, without waiting for surrounding tiles.
2Measurement precision
If waiting for surrounding frames to be completed for super-sampling processing, then the accuracy of edge handling is improved, but the resource consumption is increased
Solution Approach 1:
The patent applies different processing strategies to different regions within tiles. Edge regions of tiles receive enhanced processing with careful handling of boundary conditions, while interior regions use standard super-sampling. This local quality approach ensures edge accuracy is maintained where needed while reducing overall resource consumption.
Solution Approach 2:
Each tile is designed to be self-sufficient for super-sampling processing, containing all necessary information within the tile boundaries or using locally available data. This eliminates the need to wait for or access data from surrounding tiles, reducing memory access operations and resource consumption while maintaining processing accuracy.
3Measurement precision
If performing shading on all pixels using shader module, then the rendering quality is improved, but the processing speed is reduced
Solution Approach 1:
The patent performs full shading operations only on critical regions such as tile edges and regions containing important geometric features. For other regions, particularly interior regions of tiles with simple characteristics, the system uses simplified shading or relies on super-sampling to achieve adequate quality. This partial action approach maintains rendering quality where necessary while significantly improving processing speed.
Solution Approach 2:
The patent segments the shading process by identifying and separately handling different regions within tiles. Edge regions, feature-containing regions, and interior regions are processed differently. This segmentation allows the system to apply computationally intensive shading only where necessary while using faster methods elsewhere, balancing quality and speed.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A processor-implemented method included determining first color values of a portion of pixels of plural pixels, the portion of pixels being in a tile frame corresponding to a partial region of an input frame, by performing shading using a shader module on the portion of pixels included, determining second color values of other pixels, the other pixels being pixels of the plural pixels not included in the portion of pixels of the tile frame, by performing neural network-based super-sampling processing on the other pixels, and determining a rendered tile frame including the first color values of the portion of pixels and the second color values of the other pixels, the determining of the first color values of the portion of pixels including determining edge color values of pixels in an edge region by performing shading using the shader module on the pixels in the edge region of the tile frame.