Tile-Based Image Rendering with Edge Shading and Neural Super-Sampling
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Solution Overview
Problem
Existing image rendering methods using GPUs and neural networks face inefficiencies due to the need for larger input dimensions and lengthy processing times, particularly when performing super-sampling on tile frames, which require data from surrounding frames and lead to increased resource consumption and processing delays.
Innovation Solution
A method and apparatus that utilize a shader module for edge region shading and a neural network-based super-sampler for non-edge region processing, determining color values independently within each tile frame without relying on surrounding frames, optimizing resource use and reducing processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network-based super-sampling processing is performed on all pixels of a tile frame, then image quality is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent divides the tile frame into two distinct regions: an edge region and a non-edge region. The edge region contains pixels requiring high-quality rendering for sharp boundaries, while the non-edge region contains pixels that can be processed more efficiently. This segmentation allows the system to apply different processing strategies to different parts of the image, improving overall efficiency while maintaining quality where needed.
Solution Approach 2:
The patent applies local quality by performing complete neural network-based super-sampling processing only on the edge region where high image quality is critical, while using a simplified or alternative processing method for the non-edge region. This ensures that computational resources are concentrated on areas that most impact perceived image quality, reducing overall processing time without sacrificing where it matters most.
2Manufacturing precision
If shading is performed on all pixels using a shader module, then rendering accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent segments the pixel population into edge pixels and non-edge pixels, applying full shading processing only to the edge region where rendering accuracy is most important for maintaining image quality. The non-edge region uses reduced or alternative processing, significantly reducing the total computational workload and resource consumption while preserving accuracy where it impacts the image most.
Solution Approach 2:
The patent applies partial action by performing complete shading processing on only the necessary edge region rather than all pixels. This partial application of the resource-intensive shading operation is sufficient to achieve the desired rendering accuracy for critical areas, while avoiding the waste of resources on regions that do not require such intensive processing.
3Reliability
If tile frame processing requires data from surrounding frames, then rendering completeness is improved, but device complexity increases
Solution Approach 1:
The patent extracts and processes only the necessary information within the current tile frame boundaries, eliminating the dependency on surrounding frame data. By taking out the processing requirement for external frame data, the system achieves rendering completeness for the current frame independently, significantly reducing the complexity of data management and processing coordination.
Solution Approach 2:
The patent enables self-service by allowing each tile frame to be processed independently using only its own pixel data, without requiring assistance or data from surrounding frames. This self-contained processing approach simplifies the overall system architecture by removing inter-frame data dependencies while maintaining complete and reliable rendering of each tile frame.
Data Source
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.