Sequential Neural Networks for Efficient Image Parameter Adjustment
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Solution Overview
Problem
Conventional automated image adjustment systems rely on large neural networks that require extensive training data, memory, and processing time, making them inefficient for adjusting multiple image aspects.
Innovation Solution
The use of multiple small neural networks trained sequentially to identify parameter image adjustments by rendering images with candidate parameter values and analyzing the resulting variations, allowing for more efficient prediction and adjustment of image parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large neural networks are used for automated image adjustment, then the system can handle multiple image aspects, but the training time, memory requirements, and processing time increase significantly
Solution Approach 1:
The patent divides a single large neural network into multiple smaller neural networks, each responsible for adjusting a specific image parameter (e.g., exposure, contrast, sharpness). This segmentation allows each small network to be trained independently and efficiently on specialized data, reducing overall training time and memory requirements while maintaining the capability to adjust multiple image aspects through coordinated execution of the specialized networks
2Adaptability or versatility
If large neural networks are used for automated image adjustment, then comprehensive image processing is achieved, but the memory requirements increase significantly
Solution Approach 1:
The patent segments the memory requirements by assigning each small neural network to handle specific parameter adjustments. Each small network requires minimal memory for its specialized parameters, and the system only needs to load and execute the relevant small networks based on the image adjustment needs, significantly reducing peak memory requirements compared to loading a single large network that must accommodate all parameters simultaneously
3Measurement precision
If manual image adjustment is performed, then precise control over each aspect is achieved, but the process is time consuming
Solution Approach 1:
The patent implements self-service by training each small neural network to autonomously determine optimal parameter values for its specific function. When processing an image, the system automatically executes the relevant small networks in sequence, with each network independently making precise adjustments to its designated parameter without requiring manual intervention, thereby achieving both precision and high processing speed
Data Source
AI summary
Methods and systems are provided for identifying parameter image adjustments. In embodiments, a set of candidate parameter values associated with a parameter to be analyzed in association with an image is identified. Subsequently, the image is rendered in accordance with each candidate parameter value to generate a set of rendered images. A neural network can then be used to identify a parameter image adjustment to apply to the image based on features associated with the set of rendered images. The neural network can be trained based on a comparison of the identified parameter image adjustment and a reference parameter value associated with the parameter being analyzed.