Image Signal Processor Optimized for Computer Vision Analysis
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Solution Overview
Problem
Conventional image signal processors are optimized for aesthetic human viewing and hinder effective analysis by computer vision systems, as they do not produce images suitable for computer vision processing.
Innovation Solution
An image signal processor utilizing a first neural network to generate post-processed images, aided by a discriminator neural network trained to differentiate between real and fake images, ensuring the post-processed images are recognizable as real by the discriminator, thereby enhancing computer vision analysis capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If an image signal processor processes images for aesthetic optimization, then the visual appeal for human eyes is improved, but the suitability for computer vision processing deteriorates
Solution Approach 1:
The invention segments the image processing function into two distinct pathways: one optimized for aesthetic purposes and another optimized for computer vision analysis. This is achieved through separate processing modules or configurable processing modes that can selectively apply different processing algorithms and parameters, allowing the system to serve both human visual preferences and machine analysis requirements simultaneously
Solution Approach 2:
The image signal processor is designed with multi-functionality to handle both aesthetic optimization and computer vision processing requirements. By incorporating adjustable processing parameters, multiple processing modes, and configurable algorithm selections, the processor can adapt its behavior based on the intended application, making it universally applicable to both human viewing and machine analysis tasks
2Manufacturing precision
If conventional image processing is applied, then aesthetic quality is improved, but analysis accuracy for computer vision deteriorates
Solution Approach 1:
The invention applies local quality by implementing different processing characteristics in different regions or aspects of the image processing pipeline. Specifically, certain processing stages maintain high-fidelity, minimal processing to preserve analysis accuracy, while other stages apply aesthetic enhancements. This allows different parts of the processing system to have optimized qualities suited to their specific functions
Solution Approach 2:
The image signal processor incorporates dynamic configurability where processing parameters, algorithm selections, and processing intensity can be adjusted in real-time based on the application requirements. This dynamic adaptation allows the system to switch between aesthetic-optimized and analysis-optimized processing modes, or to balance both objectives according to specific task demands
Data Source
AI summary
An image processing system includes: an image signal processor including a first neural network, and processing an input image by using the first neural network so as to generate a post-processed image; and a discriminator including a second neural network, and receiving a target image and the post-processed image, and discriminating the target image and the post-processed image into a real image and a fake image by using the second neural network, wherein the second neural network is trained to discriminate the target image as a real image and to discriminate the post-processed image as a fake image, and the first neural network is trained in such a manner that the post-processed image is discriminated as a real image by the second neural network.