User Adaptive Image Compensator for Personalized Display
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Solution Overview
Problem
Conventional image compensation methods based on universal preferences do not adequately cater to individual user preferences, leading to suboptimal image processing.
Innovation Solution
A user-adaptive image compensator system that includes a feature extractor, a compensated image generator, an image selector, and a preference parameter updater, which learns and applies individual user preferences to generate customized compensated images based on extracted features and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If universal preference-based image compensation is applied, then image processing can be executed efficiently with simple algorithms, but the compensation results do not match individual user preferences
Solution Approach 1:
The system performs preliminary actions by pre-processing images to extract features and pre-defining multiple compensation curves with different characteristics. These prepared elements are stored and later combined based on user preferences, avoiding the need for complex real-time calculations while achieving personalized compensation results.
Solution Approach 2:
The compensation system dynamically adapts to individual user preferences by allowing users to select and weight different compensation curves based on their personal tastes. The system dynamically combines multiple pre-defined curves in varying proportions to generate customized compensation results for each user, making the compensation adaptable rather than static.
2Measurement precision
If multiple compensation curves are generated and displayed to users for selection, then user preference accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
Multiple compensation curves are pre-calculated and stored before user interaction. This preliminary preparation allows the system to quickly retrieve and combine these curves when a user provides feedback, avoiding the need to generate multiple compensation versions in real-time and significantly reducing processing time.
Solution Approach 2:
The system generates a set of compensation curves that covers a range of possible user preferences (excessive action), then allows users to select from these pre-generated options. This approach provides high preference measurement accuracy through multiple choices while avoiding the time cost of generating all possible compensations on-demand.
3Adaptability or versatility
If user feedback is continuously collected and preference parameters are dynamically updated, then individualization of compensation is improved, but computational complexity and processing overhead increase
Solution Approach 1:
The system pre-defines multiple compensation curves with distinct characteristics before user interaction begins. This preliminary setup allows the system to later determine user preferences by comparing selections against these pre-prepared curves, avoiding the need for complex automated learning algorithms while still achieving individualized compensation through user feedback.
Data Source
AI summary
A user adaptive image compensator includes a feature extractor, a compensated image generator, an image selector, and a preference parameter updater. The feature extractor extracts features from an input image. The compensated image generator generates compensated preference parameters based on a preference parameter. The compensated image generator generates a plurality of compensated images by compensating the input image based on the compensated preference parameters. The image selector displays the compensated images to a user. The image selector outputs a selected compensated image, which is selected from the compensated images by the user, as an output image. The image selector outputs a selected compensated preference parameter from the compensated preference parameters and which corresponds to the selected compensated image. The preference parameter updater updates the preference parameter based on the selected compensated preference parameter and the extracted features.