Kernel Prediction Network HDR Image Processing
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Solution Overview
Problem
High computational intensity and memory usage of convolutional neural networks (CNNs) for high-resolution image processing, particularly for high dynamic range (HDR) content, lead to impractical solutions and loss of information due to quantization, while kernel prediction networks (KPNs) face challenges in efficiently representing HDR ranges and consuming limited memory resources.
Innovation Solution
Implementing a kernel prediction network (KPN) with a kernel regression component and a lightweight filtering component, using a kernel lookup network (KLN) to derive kernel coefficients from predefined sets, allowing for efficient processing and reduced memory usage by leveraging volumetric data structures and interpolation between kernel values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolutional neural networks (CNNs) are used for high-resolution image processing, then processing accuracy is improved, but computational intensity and memory usage increase to impractical levels
Solution Approach 1:
The patent segments the kernel coefficient generation process into two parts: a neural network component that determines kernel characteristics from image data, and a lookup table component that stores pre-computed kernel values. This segmentation allows the system to achieve high processing accuracy through neural network analysis while reducing computational intensity by using efficient table lookups for actual kernel application.
Solution Approach 2:
The patent creates copies of kernel values in lookup tables that can be quickly retrieved and applied to image processing. Instead of computing kernel coefficients from scratch for every pixel, the system uses pre-computed kernel copies stored in memory, significantly reducing the computational burden while maintaining processing accuracy.
2Measurement precision
If convolutional neural networks (CNNs) are used for high-resolution image processing, then processing accuracy is improved, but memory consumption increases to impractical levels
Solution Approach 1:
The patent segments memory usage into two functional areas: neural network parameters that are relatively small and stable, and lookup tables that store only the necessary kernel values. This segmentation reduces overall memory consumption compared to storing complete CNN weights for every possible input condition, while maintaining processing accuracy.
Solution Approach 2:
The patent uses lookup tables to store copies of kernel values that can be quickly retrieved during image processing. This approach reduces memory consumption compared to maintaining full CNN models, as only the essential kernel information is stored rather than complete network parameters for all possible inputs.
3Measurement precision
If kernel prediction networks (KPNs) are used to represent HDR ranges, then HDR representation accuracy is improved, but memory resources are consumed excessively
Solution Approach 1:
The patent segments the HDR representation into two components: neural network-based prediction of kernel characteristics that provides accuracy, and lookup tables that store compressed kernel values. This segmentation enables accurate HDR representation while reducing memory resources by avoiding the need to store complete high-precision kernel data for all possible HDR input conditions.
Solution Approach 2:
The patent uses lookup tables to store compressed copies of kernel values that represent HDR ranges. Instead of storing full-precision kernel data for every possible HDR input, the system uses these compressed copies, reducing memory resource consumption while maintaining accurate HDR representation through intelligent kernel selection and interpolation.
4Productivity
If kernel prediction networks (KPNs) are used for image processing, then processing efficiency is improved, but memory resources are consumed excessively
Solution Approach 1:
The patent segments the system into a neural network component that efficiently analyzes image data and determines kernel characteristics, and a lookup table component that provides rapid access to pre-computed kernel values. This segmentation achieves processing efficiency through the neural network's intelligent kernel selection while reducing memory resources by using compact lookup tables instead of storing complete kernel datasets.
Solution Approach 2:
The patent uses lookup tables to store compressed copies of kernel values that can be quickly retrieved during processing. This approach maintains processing efficiency by enabling rapid kernel application while reducing memory resource consumption compared to storing complete high-precision kernel data for all possible input conditions.
Data Source
AI summary
Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, techniques to process image signal intensity values sampled from a multi color channel imaging device. In particular, methods and/or techniques disclosed herein are directed to processing image signal intensity values by application of kernel coefficients to the image signal intensity values.