Selective Ray Tracing for AI Upsampled Image Regions
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Solution Overview
Problem
Current graphics processing technologies face challenges in correcting image regions after upsampling or frame interpolation, particularly in real-time applications where confidence in AI-based upsampling is low, leading to potential noise and quality issues.
Innovation Solution
Combining AI-based upsampling with selective ray tracing, where areas of low confidence are recalculated using ray tracing to enhance image quality, efficiently utilizing graphics processing resources by focusing on specific patches or frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If AI-based upsampling is applied to all image regions, then image quality is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent applies AI-based upsampling selectively to specific image regions rather than uniformly across the entire image. The system identifies and processes only those regions that require enhancement, leaving other regions to be handled by traditional upsampling methods. This localized approach maintains image quality in critical areas while reducing overall processing time and computational resource consumption.
2Manufacturing precision
If ray tracing is applied to correct image regions, then image quality is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies ray tracing selectively to only those image regions where AI-based upsampling confidence is low or where correction is most needed, rather than applying it to the entire image. This partial application of the computationally intensive ray tracing technique maintains image quality in problematic areas while preserving overall processing throughput and productivity.
3Productivity
If selective correction is applied to low confidence regions, then resource efficiency is improved, but image quality may deteriorate in uncorrected regions
Solution Approach 1:
The patent introduces a confidence assessment mechanism that acts as an intermediary between AI-based upsampling and ray tracing correction. This intermediary evaluates the quality and reliability of AI-upsampled regions and directs ray tracing correction only to regions that fall below quality thresholds, ensuring that image quality is maintained in problematic areas while resources are efficiently allocated.
Data Source
AI summary
Apparatus and method for correcting image regions following upsampling or frame interpolation. For example, one embodiment of an apparatus comprises a machine-learning engine to evaluate at least a first image in a sequence of images generated by a real-time interactive application, the machine learning engine to responsively use previously learned data to generate an upsampled or interpolated image comprising a plurality of pixel patches. In one embodiment, each pixel patch is associated with a confidence value reflecting how accurately the pixel patch was generated by the machine learning engine. A selective ray tracing engine identifies a first pixel patch to be corrected based a first confidence value corresponding to the first pixel patch being lower than a threshold and performs ray tracing operations on a first portion of the first image to generate a corrected first pixel patch.