Neural Network Image Enhancement via Low-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 first, 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 image and video content is generated directly, then output quality is improved, but processing resources and computational complexity increase significantly
Solution Approach 1:
The image processing pipeline is segmented into distinct stages: generating lower-resolution content first, then separately applying visual effects, and finally upsampling the result. This segmentation allows each stage to operate at reduced computational complexity while maintaining overall output quality through the composition of results from simpler operations.
Solution Approach 2:
Visual effects are applied to lower-resolution content before upsampling to high resolution. This preliminary action reduces the computational burden during the effect application stage, as processing at lower resolution requires fewer resources. The final high-resolution output is achieved by upsampling the processed low-resolution result rather than processing at high resolution throughout.
2Manufacturing precision
If visual effects are applied to high-resolution content, then output quality is improved, but processing time and latency increase
Solution Approach 1:
Visual effects are applied to lower-resolution content as a preliminary step before upsampling. This timing optimization ensures that computationally intensive effect application occurs when data volume is smaller, reducing overall processing time. The pipeline structure allows effects to be processed once at low resolution, then the result is efficiently upsampled to high resolution, avoiding the time cost of processing effects at high resolution.
Solution Approach 2:
The processing pipeline separates the visual effects application stage from the upsampling stage. By segmenting these operations, the system can apply effects to reduced-resolution data faster, then separately perform the upsampling operation. This segmentation prevents the time penalty of processing effects at full resolution while maintaining final output quality.
3Productivity
If high frame rate video content is generated at high resolution, then display quality is improved, but processing capacity requirements increase
Solution Approach 1:
The video processing pipeline segments resolution handling from effects application. Content is generated at lower resolution with visual effects applied, then upsampled to the target high resolution. This segmentation enables higher frame rates at reduced processing capacity requirements, as the computationally intensive effects are applied to lower-resolution data while maintaining the ability to output at high resolution and high frame rate.
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.