Pixel Locking for Temporal Image Classification Upscaling
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Solution Overview
Problem
Conventional spatial upscalers require high-quality anti-aliased source images and suffer from inferior upscaled outputs when anti-aliasing is poorly implemented, especially in low-resolution scenarios, leading to artifacts like shimmering and poor edge reconstruction.
Innovation Solution
Implementing a super resolution upscaler that uses temporal feedback to reconstruct high-resolution images by leveraging previously upscaled frames, enhancing image quality and performance, particularly for hardware ray tracing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional spatial upscalers are used to upscale low-resolution frames, then integration is easy, but image quality deteriorates due to insufficient information for thin detail reconstruction
Solution Approach 1:
The system performs preliminary anti-aliasing processing on the low-resolution source image before upscaling. By pre-processing the image with anti-aliasing filters and generating multiple anti-aliased versions at different resolutions, the system prepares enhanced input data that enables better thin detail reconstruction during the subsequent upscaling process, thereby improving image quality without complicating integration
Solution Approach 2:
The system introduces an intermediary processing stage that generates multiple anti-aliased source images at different resolutions between the original low-resolution input and the final upscaled output. These intermediate anti-aliased images serve as enhanced input data for the upscaler, providing sufficient information for accurate thin detail reconstruction while maintaining ease of integration through a modular processing pipeline
2Manufacturing precision
If anti-aliasing is implemented to improve upscaling quality, then upscaled output quality improves, but implementation time increases
Solution Approach 1:
The system applies partial anti-aliasing processing by generating only the necessary anti-aliased versions at specific resolutions required for the upscaling process, rather than applying full anti-aliasing to all possible resolutions. This selective approach achieves sufficient upscaling quality while minimizing the time investment required for anti-aliasing implementation
3Manufacturing precision
If source resolution is increased to improve thin detail reconstruction, then upscaling quality improves, but processing performance deteriorates
Solution Approach 1:
The system transitions from spatial upscaling alone to a temporal-spatial approach by incorporating multiple anti-aliased source images at different resolutions and temporal frames. This dimensional expansion allows the upscaler to draw upon additional information from multiple sources, enabling accurate thin detail reconstruction while maintaining the original low-resolution rendering performance
Data Source
AI summary
A first frame of a video stream is obtained. The first frame is defined by a plurality of pixels associated with a set of color data. A determination is made that a pixel of the plurality of pixels comprises high-frequency information. Responsive to the determination that the pixel comprises high-frequency information, a pixel lock is generated for the pixel such that color data associated with the pixel is maintained during a color accumulation process for at least one of the first frame or a second frame of the video stream that is subsequent to the first frame.