Tiled Image Rendering Pipeline for Reusing GPU Texture Tiles
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Solution Overview
Problem
Traditional GPU-based rendering approaches for large images, such as those used in medical imaging, suffer from slow data transfer and inefficient processing, leading to unacceptably slow rendering on high-resolution monitors and inhibited performance due to the large amount of data transferred between CPU and GPU.
Innovation Solution
A GPU-based rendering pipeline that utilizes a texture pyramid with toroidal wrapping, where the texture serves as both an image object and a cache, allowing reuse of previously uploaded tiles and reducing the need for re-uploading, combined with a client-server architecture to optimize data transfer and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional GPU-based rendering approaches are used for large images, then the rendering can be performed on high-resolution monitors, but the data transfer between CPU and GPU is excessively large and slow
Solution Approach 1:
The patent divides the large image into multiple smaller tiles that can be independently processed and transferred. Instead of transferring the entire large image at once, the system segments it into manageable chunks (tiles) that are transferred separately to the GPU, significantly reducing the data transfer volume at any given time while maintaining rendering capability on high-resolution monitors
Solution Approach 2:
The patent implements a caching mechanism where image tiles are pre-loaded and stored in a cache before being needed for rendering. This preliminary action allows the system to have tiles ready in advance, reducing the need for frequent data transfers during interactive operations like panning and zooming, thereby improving rendering speed while minimizing data transfer volume
2Productivity
If traditional rendering approaches are used, then complete images can be rendered, but the processing time is unacceptably slow
Solution Approach 1:
The rendering process is segmented into independent tile-based operations rather than processing the entire image as a single unit. This allows parallel processing of multiple tiles and enables the system to render only the visible portion of the image at any given time, significantly improving rendering efficiency while reducing processing time for interactive operations
Solution Approach 2:
The patent implements a view frustum culling mechanism that renders only the tiles currently visible in the viewport rather than rendering the entire image. This partial action approach focuses computational resources on only the necessary portions of the image, dramatically improving rendering efficiency and reducing processing time while maintaining complete image availability when needed
3Reliability
If all image tiles are uploaded to GPU texture, then complete image rendering is possible, but memory usage and transfer overhead increase
Solution Approach 1:
The patent manages image data as separate, independently addressable tiles rather than as a single large texture. This segmentation allows the system to upload only the necessary tiles to GPU memory at any given time, reducing memory usage and transfer overhead while maintaining the ability to reconstruct the complete image when needed by assembling the tile segments
Solution Approach 2:
The patent implements a unified texture atlas system that can accommodate multiple tiles and handle both individual tile rendering and complete image rendering through the same infrastructure. This multi-functional approach simplifies memory management by providing a consistent interface for both partial and complete image operations, reducing device complexity despite the segmented approach
Data Source
AI summary
Features are disclosed for rendering an image using a GPU and CPU based rendering pipeline. An imaging system may include a GPU and a CPU that each include a portion of an image renderer. A component of the GPU can process the set of image tiles to generate a texture with a wrapped tile coordinate. A component of the GPU can further store the image data as a level of a texture pyramid. As subsequent image data is received, a component of the GPU can access the texture pyramid to determine previously stored image tiles. A component of the GPU can use the previously stored image tiles in rendering the subsequent image data.