Neural Image Enhancement for Low-Latency High-Resolution Effects
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Solution Overview
Problem
Generating high-resolution image and video content is resource-intensive and challenging for devices with limited capacity, particularly when applying post-processing effects, which can exceed processing capabilities and timing requirements.
Innovation Solution
A method involving generating lower-resolution content initially, applying visual effects to this lower-resolution content, and then approximating these effects onto higher-resolution content using parameterized functions or neural networks, reducing processing demands and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution content is generated directly, then image and video quality is improved, but processing resources and computational capacity requirements increase significantly
Solution Approach 1:
The system performs preliminary actions by generating lower-resolution content first, applying visual effects at this reduced resolution, and then using neural networks to upscale and transfer these effects to high-resolution output. This preliminary processing at lower resolution significantly reduces computational resource requirements while maintaining final high-quality output.
Solution Approach 2:
The patent uses neural networks to create a computational copy or representation of the visual effects that were applied to lower-resolution content. This copy is then transferred to and applied on high-resolution content, allowing the system to avoid the computationally expensive process of directly rendering effects at high resolution while preserving quality.
2Manufacturing precision
If visual effects are applied to high-resolution content directly, then output quality is improved, but processing time and latency increase
Solution Approach 1:
Visual effects are applied in advance to lower-resolution content before the upscaling step. This preliminary effect application reduces the amount of data that needs to be processed in subsequent high-resolution operations, thereby reducing overall processing time and latency while maintaining output quality.
Solution Approach 2:
The neural network creates a parameterized copy of the visual effects from the low-resolution processing and applies this copy to the high-resolution content. This copying approach avoids the time-consuming process of re-rendering effects at high resolution while preserving the quality and appearance of the effects.
3Manufacturing precision
If post-processing effects are applied at high resolution, then image quality is improved, but device capability requirements increase
Solution Approach 1:
The system performs all complex post-processing effect calculations in advance at lower resolution, where the computational burden is manageable for devices with limited capabilities. The results are then transferred to high-resolution output through neural network upscaling, allowing quality improvement without requiring the device to handle complex processing at high resolution.
Solution Approach 2:
The neural network creates a simplified representation or copy of the post-processing effects that can be applied to high-resolution content without requiring the device to perform the complex original processing at high resolution. This copying approach enables quality improvement while keeping device capability requirements manageable.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to generate images with one or more visual effects applied. In at least one embodiment, one or more visual effects are applied to one or more images having a resolution that is less than a first resolution and those visual effects approximated for one or more images having a resolution that is greater than or equal to the first resolution.