Image Processing Resource Allocation Across AI and Video Hardware
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Solution Overview
Problem
Existing image processing technologies face challenges in efficiently adapting to diverse image properties and characteristics due to fixed operation orders and resource wastage in hardware-based and neural network-based approaches, leading to performance degradation and redundancy.
Innovation Solution
An image processing apparatus and method that dynamically allocates resources between hardware-based image processing circuits and neural networks based on input image properties, using a first processor to determine resource allocation for a second processor to perform neural networks and a video processor for hardware-based processing, generating quality-processed images through a combination of AI and hardware-based methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hardware-based image processing circuits are used, then processing speed is improved, but adaptability to diverse image properties deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation where the system can flexibly adjust the distribution of processing tasks between hardware-based image processing circuits and neural network-based processors based on input image characteristics. This allows the system to adapt its processing approach in real-time, combining the speed of hardware circuits with the adaptability of neural networks for different image properties.
2Adaptability or versatility
If neural network-based image processing is used, then adaptability to diverse image properties is improved, but resource consumption increases
Solution Approach 1:
The patent segments the image processing workload by dividing tasks between hardware-based processing circuits and neural network-based processors. The system analyzes input image characteristics and allocates specific processing operations to the most appropriate platform, using hardware circuits for routine operations and neural networks for complex adaptive tasks, thereby optimizing resource consumption while maintaining adaptability.
3Device complexity
If fixed operation order in image processing circuits is used, then device complexity is reduced, but productivity deteriorates
Solution Approach 1:
The patent implements a dynamic operation ordering mechanism where the system can adjust the sequence of image processing operations based on input image characteristics and processing requirements. This allows the system to optimize processing pipelines in real-time, improving productivity without significantly increasing device complexity by using software-controlled scheduling rather than hardwired fixed sequences.
Data Source
AI summary
An image processing apparatus includes at least one memory, a first processor, a second processor, and a video processor, wherein the first processor is configured to obtain an input image and information about the input image, determine a resource allocation amount of the second processor to execute at least one neural network executable by the second processor, control the second processor to generate a first quality-processed image through the at least one neural network, control the video processor to generate a second quality-processed image with respect to an image input to the video processor, and generate an output image through at least one of the first quality processing or the second quality processing.