Content-Dependent Super-Resolution With Selective Neural Processing
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Solution Overview
Problem
Conventional real-time image processing techniques, particularly super-resolution on resource-limited devices like smartphones, face challenges due to high computational demands of neural network-based methods, leading to poor quality and slow performance.
Innovation Solution
A method and system that adaptively select between neural network and non-neural network algorithms for image processing based on image content analysis, using a lightweight video content analysis unit to divide images into sections and apply neural networks only where texture information is sufficient, reducing unnecessary computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If neural network-based super-resolution techniques are used to achieve high image quality, then image quality is improved, but computational load and power consumption increase significantly
Solution Approach 1:
The patent applies different processing algorithms to different regions of the image based on local characteristics. Texture analysis is performed on each image block, and neural network super-resolution is applied only to regions with sufficient texture information, while smooth regions use conventional interpolation methods. This selective application reduces overall computational load and power consumption while maintaining high quality where needed.
2Manufacturing precision
If neural network-based super-resolution techniques are used to achieve high image quality, then image quality is improved, but processing speed decreases
Solution Approach 1:
The patent divides the image into multiple blocks and performs texture analysis on each block. Neural network processing is applied only to blocks with sufficient texture complexity, while smooth blocks use fast conventional interpolation. This selective approach significantly reduces total processing time while maintaining high quality output, enabling real-time performance on resource-limited devices.
3Productivity
If conventional real-time super-resolution techniques are used to reduce computational load, then processing speed is improved, but image quality deteriorates
Solution Approach 1:
The patent performs local texture analysis on each image block and selectively applies neural network super-resolution only to regions where it provides meaningful quality improvement. Smooth regions use fast conventional interpolation, while textured regions use the computationally intensive neural network. This hybrid approach achieves both real-time processing speed and high image quality where needed.
Solution Approach 2:
The patent divides the image into multiple blocks and processes each block independently based on its local texture characteristics. This segmentation allows different algorithms to be applied to different regions, optimizing both speed and quality for each local area while maintaining overall processing efficiency.
4Manufacturing precision
If neural network-based image processing is applied to all image regions, then image quality is improved, but computational cost increases unnecessarily
Solution Approach 1:
The patent performs texture analysis on each image block to determine local complexity. Neural network processing is applied only to blocks with sufficient texture information where it can produce meaningful quality improvement. Smooth blocks use simple conventional interpolation methods. This selective application eliminates unnecessary computational cost while maintaining high quality where it matters.
Data Source
AI summary
A method, system, and article is directed to automatic content-dependent image processing algorithm selection.