Neural Network Color Preference Editing for Personalized Image Correction
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Solution Overview
Problem
Existing methods for editing color preferences are inefficient as they provide general color preference transformation functions, making it difficult and time-consuming to tailor color corrections to individual user preferences within predefined color spaces.
Innovation Solution
An apparatus and method that utilize a neural network to learn and predict color information variations by extracting data from user-preferred transformations of original images, allowing for personalized color corrections in input images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general color preference transformation functions are provided, then color correction can be applied to images, but it is difficult to tailor color corrections to individual user preferences and significant time is required for editing
Solution Approach 1:
The system performs preliminary actions by training a neural network model in advance with user preference data. The neural network learns and stores color transformation patterns specific to individual users during a training phase, so that when actual color correction is needed, the pre-learned preferences can be quickly applied without requiring time-consuming manual adjustment of color parameters
Solution Approach 2:
The patent introduces a neural network as an intermediary between the user's preference data and the color correction process. The neural network acts as a mediator that automatically interprets user preferences and generates appropriate color transformations, eliminating the need for users to manually adjust color parameters and significantly reducing editing time
2Ease of operation
If manual color preference editing is provided, then color corrections can be customized, but the process is time-consuming and complex
Solution Approach 1:
The system implements self-service by enabling the neural network to automatically learn and adjust color preferences without requiring manual user intervention. The neural network autonomously processes user feedback and preference data to generate optimized color transformations, making the system self-adjusting and eliminating the need for users to understand or manually configure complex color parameters
Solution Approach 2:
The patent replaces the manual mechanical process of adjusting color parameters with an automated neural network system. Instead of users manually sliding controls or inputting numerical values to adjust color preferences, the neural network automatically processes visual feedback and generates color transformations, substituting the mechanical interaction with an intelligent automated system
Data Source
AI summary
An apparatus and method for editing an optimized color preference are provided. The apparatus includes a color information controlling unit which extracts data about a preference by comparing color information of a transformed image generated by transforming color information of an original image and the original image according to a user preference; a learning unit which teaches a neural network about the preference, based on the extracted data, and predicts color information variation by the neural network; and an image correcting unit which corrects color information of an input image according to the predicted color information variation. The method includes extracting data about a preference; teaching a neural network about the preference, based on the extracted data; predicting color information variation by the neural network; and correcting color information of an input image according to the predicted color information variation.