Information Processing Device for Automated Learned-Model Image Gradation
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Solution Overview
Problem
Existing methods for gradation processing on image data, such as shadow, oblique line, tone, and special print processing, are inefficient and labor-intensive, requiring skilled personnel and unable to handle a large volume of work in a timely manner.
Innovation Solution
An information processing device and method that utilizes a learned model to automatically perform gradation processing on image data, incorporating user input for hint information and light source position, and outputs mask channels for processing based on a neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If gradation processing is performed manually by skilled personnel, then processing quality and accuracy are maintained, but productivity is low and large volumes of work cannot be completed in limited time
Solution Approach 1:
The patent replaces manual mechanical gradation processing with an automated image processing system using a neural network. The system automatically analyzes input images, determines appropriate gradation processing, and generates output images without manual intervention, thereby dramatically improving productivity while maintaining consistent quality through algorithmic processing rather than human skill variation
Solution Approach 2:
The system enables self-service gradation processing by allowing users to input images and receive automatically processed outputs without requiring skilled operators. The neural network model performs the complex gradation determination autonomously, making the process accessible to anyone with basic computer operation skills while maintaining professional-quality results
2Manufacturing precision
If manual gradation processing is used, then processing accuracy and quality control are maintained, but loss of time increases and efficiency decreases
Solution Approach 1:
The system performs preliminary learning and model training in advance to establish the neural network's gradation processing capabilities. Once trained, the model can rapidly process images without requiring real-time human analysis, thereby reducing processing time while maintaining the accuracy standards established during the learning phase
Solution Approach 2:
The patent replaces time-consuming manual analysis and processing with automated neural network computation. The system rapidly analyzes image features, determines appropriate gradation, and generates outputs much faster than manual processing while maintaining consistent accuracy through the learned model's systematic approach
3Productivity
If automated gradation processing is implemented, then productivity and processing speed are improved, but device complexity increases due to need for learned models and neural networks
Solution Approach 1:
The patent extracts the complex neural network model creation and training process from the routine image processing workflow. The model is trained separately in advance using learning data, and then the trained model is deployed for efficient batch processing. This separation allows the system to handle large volumes of images with simple automated processing while the complexity of model development occurs independently during the learning phase
Solution Approach 2:
The system uses a universal neural network model that can handle various types of gradation processing tasks through learned patterns. The single trained model serves multiple processing needs, reducing the overall system complexity compared to having separate specialized processors for different gradation types, while maintaining high productivity across diverse image processing requirements
Data Source
AI summary
An information processing device includes a memory, and processing circuitry coupled to the memory. The processing circuitry is configured to acquire gradation processing target image data, and perform gradation processing on the gradation processing target image data based on a learned model learned in advance.