Curvy RHT Decomposition for Efficient Camera Phone Rendering
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Solution Overview
Problem
Conventional camera phones have limitations in image resolution and processing power due to their compact size and stringent power requirements, which restricts their ability to handle complex video processing and high-quality image rendering.
Innovation Solution
A system and method for decomposing complex shapes into curvy RHTs (right horizontal trapeziums) for rasterization, utilizing Bezier curves and curve segments to enable efficient tile-based rendering and color processing, allowing for improved handling of complex graphics and video processing in camera phones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex shapes are rendered using conventional rasterization methods, then image quality and rendering accuracy are improved, but processing power requirements and power consumption increase significantly
Solution Approach 1:
The patent divides complex curved shapes into multiple simpler curvy RHT primitives, each of which can be processed independently. This segmentation allows the rendering system to handle complex shapes by processing multiple simpler units in parallel, reducing the computational burden on any single processing unit while maintaining overall rendering accuracy.
Solution Approach 2:
The patent introduces a tile-based rendering approach that adds a spatial dimension to the processing architecture. By organizing rendering into tiles that can be processed in parallel, the system transforms a sequential processing problem into a parallel one, effectively increasing processing capacity without requiring a single more powerful processor.
2Measurement precision
If high-resolution image sensors and advanced processing are incorporated, then image resolution is improved, but device size and power requirements increase
Solution Approach 1:
The patent segments the image processing task into tile-based units that can be handled by smaller, more efficient processors. This allows high-resolution imaging to be achieved through parallel processing of multiple tiles rather than requiring a single large processor, thereby reducing overall device size while maintaining resolution.
3Ease of manufacture
If conventional shape decomposition methods are used, then implementation simplicity is maintained, but rendering efficiency and processing speed decrease
Solution Approach 1:
The patent segments shapes into curvy RHT primitives that are simple to generate and process. Each primitive maintains a regular structure that is easy to implement, while the collection of these simple units achieves efficient rendering through parallel processing, thus improving productivity without sacrificing implementation simplicity.
Solution Approach 2:
The patent uses curvy RHT primitives that incorporate curved boundaries rather than strict rectangular shapes. This curvature allows the primitives to better approximate complex shapes with fewer units, improving rendering efficiency while maintaining ease of generation through standardized curve-based operations.
Data Source
AI summary
A complex curved primitive is decomposed into curvy RHTs comprising a curved portion and horizontal and vertical lines. Pixel rows covered by curvy RHTs are determined. RHT pixels covered by the curved primitive are determined with counters. Tile based color rendering is performed for covered pixels. The primitive path is decomposed into Bezier curves. Curvy RHTs may overlap and may cover pixels that are not covered by the curved primitive. Pixel rows are located by traversing an RHT path and the direction of traversing may determine pixel counts. Pixel coverage and tile coverage information is stored in memory. Pixels may be rendered in parallel. The curved primitive is rendered in a tile binning phase and a tile rendering phase. Tile rendering comprises a pixel coverage first pass which determines pixels covered by said curved primitive and a color rendering second pass.