Frame-to-Frame Anti-Aliasing Using ID and Velocity Maps
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Solution Overview
Problem
Existing anti-aliasing technologies, such as SSAA and MSAA, face challenges in providing smooth images with fine geometry while being energy and cost-efficient, especially with increasing computational and memory demands.
Innovation Solution
A computer-implemented method using a data model trained with high-resolution images and ID maps to perform anti-aliasing by correlating image portions between frames, reducing the need for hardware-intensive computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If supersample anti-aliasing (SSAA) is used to improve image quality, then anti-aliasing quality is improved, but memory and computational requirements increase significantly
Solution Approach 1:
The patent performs anti-aliasing computations in advance during the rendering process, storing results in a buffer for later use. This preliminary action allows the system to avoid performing expensive anti-aliasing calculations in real-time, thereby reducing computational requirements while maintaining quality.
Solution Approach 2:
The patent creates a copy of the image data in a buffer structure that stores anti-aliased values separately from the main rendering buffer. This copying approach allows the system to reuse pre-computed anti-aliasing data without repeatedly performing expensive calculations, reducing both memory bandwidth consumption and computational overhead.
2Device complexity
If multisample anti-aliasing (MSAA) is used to reduce computation, then computational requirements are reduced, but image quality may deteriorate
Solution Approach 1:
The patent applies anti-aliasing selectively to specific regions of the image where it is most needed, rather than uniformly across the entire image. By identifying and processing only the necessary areas, the system maintains high image quality at edges and boundaries while reducing overall computational requirements.
Solution Approach 2:
The patent modifies the rendering parameters by storing anti-aliased values in a dedicated buffer with specific data structures optimized for the anti-aliasing process. This parameter change allows for more efficient memory access patterns and computational operations, improving both quality and performance.
3Manufacturing precision
If anti-aliasing is performed across the entire image, then anti-aliasing coverage is improved, but processing time and resources increase
Solution Approach 1:
The patent divides the image processing into separate stages: a first pass that identifies regions requiring anti-aliasing, and a second pass that applies the anti-aliasing computations. This segmentation allows the system to focus resources only on areas that need anti-aliasing, improving processing efficiency while maintaining comprehensive coverage where necessary.
Solution Approach 2:
The patent performs a preliminary analysis pass to identify edges and regions requiring anti-aliasing before performing the actual anti-aliasing computations. This preliminary action enables the system to optimize resource allocation and avoid unnecessary processing in areas that do not require anti-aliasing, thereby improving overall processing efficiency.
Data Source
AI summary
There is provided a computer-implemented method for anti-aliasing an image frame in an image stream. The method comprises providing an input to a data model, the data model being configured to perform anti-aliasing on a present frame which is displayed after a previous frame in the image stream. The input comprises: an anti-aliased image of the previous frame; an aliased image of the present frame; an ID map of the previous frame; an ID map of the present frame; and a velocity map based on changes between the previous frame and the present frame. The data model processes the received inputs to correlate image portions of the previous frame and image portions of the present frame based on the inputs and performing anti-aliasing on the present frame based at least in part on the correlated image portions. An anti-aliased image of the present frame is received as an output of the data model.