Image Processing With Downsampled Neural Networks for 4K Video
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Real-time image processing chips are limited in performance, preventing the use of artificial intelligence models like CNN networks for processing high-resolution inputs such as 4K or 8K films.
Innovation Solution
An image processing system that includes a preprocessing module to downsample images, a neural network module to process the downsampled tensors, and an upsampling module to restore the image to its original dimensions, with an addition module for element-by-element addition, reducing input dimensions and computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network module processes high-resolution images directly, then image processing quality is improved, but computational load and energy consumption increase beyond chip performance limits
Solution Approach 1:
The processing system is segmented into multiple specialized modules: a preprocessing module for downscaling, a neural network module for feature extraction, an upsampling module for resolution restoration, and an addition module for final composition. This segmentation allows each module to operate efficiently on optimized data sizes, reducing overall computational load and energy consumption while maintaining processing quality.
Solution Approach 2:
The preprocessing module acts as an intermediary that transforms the original high-resolution image into a downsampled tensor suitable for neural network processing. The upsampling module serves as another intermediary that restores the processed features back to the original resolution. These intermediary modules enable the neural network to work with reduced data dimensions, significantly lowering energy consumption while preserving essential image information.
2Measurement precision
If a neural network module processes high-resolution images directly, then image processing quality is improved, but buffer requirements exceed available memory resources
Solution Approach 1:
The buffer memory is segmented and allocated to different modules based on their specific needs. The preprocessing module requires minimal buffer for downscaling operations, the neural network module receives pre-processed compact tensors, and the upsampling module operates on intermediate representations. This segmented buffer allocation dramatically reduces the peak memory requirements compared to processing the entire high-resolution image in one module.
Solution Approach 2:
The preprocessing module serves as an intermediary that reduces the data volume before it enters the memory-constrained neural network module. By downscaling the image to a smaller tensor representation, the intermediary preprocessing step ensures that subsequent modules operate within available buffer constraints while still preserving the essential features needed for quality processing.
3Measurement precision
If the neural network structure is deepened to improve processing capability, then image processing quality is improved, but computational complexity exceeds real-time processing requirements
Solution Approach 1:
The computational workload is segmented across multiple passes and modules rather than requiring an extremely deep single network. The preprocessing module handles initial transformations, the neural network module performs feature extraction at reduced resolution, and the upsampling module completes the processing. This segmentation allows for a moderately deep network that can achieve high processing quality while meeting real-time speed requirements.
Solution Approach 2:
The preprocessing module performs preliminary actions by downscaling and pre-processing the image before it enters the neural network. This preliminary reduction in data dimensionality allows the subsequent neural network to be moderately deep without exceeding computational limits, as the network operates on already-optimized input data that requires fewer processing stages.
Data Source
AI summary
An image processing system, an image processing method, and a training system are provided. The image processing method includes: receiving, by a preprocessing module in an image processing module, an image, and downsampling the image to obtain a downsampled tensor; processing, by a neural network module in the image processing module based on a plurality of first parameters, the downsampled tensor and generating an output tensor; upsampling, by an upsampling module in the image processing module, the output tensor to generate an upsampled tensor having same dimensions as the image; and performing, by an addition module, element-by-element addition on the upsampled tensor and the image to obtain an output image.