AI Image Cropping by Aesthetic Region Selection
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Solution Overview
Problem
Existing image editing systems require manual human intervention to adapt images to different platform-specific configurations while maintaining aesthetic quality, which is time-consuming and skill-dependent.
Innovation Solution
An AI engine identifies candidate regions in an image, generates regional proposal images based on visual features, evaluates their aesthetic value, and selects the most aesthetically pleasing variant to meet desired configurations automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image processing techniques are used to modify images, then processing speed is fast, but the modification results lack naturalness and aesthetic quality
Solution Approach 1:
The system pre-trains multiple domain transformation models (e.g., day-to-night, summer-to-winter) on large datasets before actual use. This preliminary training allows the models to learn complex transformation patterns in advance, enabling high-quality aesthetic modifications to be applied quickly during actual image processing without requiring intensive real-time computation.
Solution Approach 2:
The system transforms images by changing multiple parameters simultaneously through learned domain transformations, including color temperature, lighting conditions, seasonal characteristics, and temporal features. By adjusting these parameters through pre-trained models rather than manual editing, the system achieves both high aesthetic quality and efficient processing speed.
2Manufacturing precision
If manual image editing is performed to achieve natural modification results, then aesthetic quality is high, but processing time is excessive
Solution Approach 1:
The system creates and stores multiple domain-transformed versions of images (e.g., different times of day, seasons, weather conditions) as pre-computed copies. When a user requests a modification, the system retrieves or lightly adjusts the appropriate pre-computed version rather than performing extensive manual editing, significantly reducing processing time while maintaining natural modification results.
Solution Approach 2:
The system replaces manual mechanical editing processes with automated neural network-based domain transformation models. These models automatically perform complex editing tasks that would require extensive manual intervention, achieving both natural modification results and dramatically reduced processing time through intelligent automation.
3Measurement precision
If multiple image processing models are trained for different domains, then transformation accuracy is improved, but system complexity increases
Solution Approach 1:
The system employs a universal framework that can handle multiple domain transformations (day/night, seasonal changes, weather conditions) through a common architecture. This multi-functional approach allows the system to maintain high transformation accuracy across different domains while managing model complexity through a unified structure rather than completely separate models for each transformation type.
Solution Approach 2:
The system segments the complex domain transformation task into multiple specialized sub-models, each handling a specific transformation (e.g., one model for day-to-night, another for seasonal changes). This segmentation allows each sub-model to be optimized for its specific domain, improving transformation accuracy while the modular structure helps manage overall system complexity through organized, manageable components.
Data Source
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AI summary
A scheme for modifying an image is disclosed, which includes receiving a source image having a first image configuration; determining a second image configuration for a target image; providing the received source image to an AI engine trained to identify, based on a set of rules related to visual features, candidate regions from the source image; generating proposal images based on the candidate regions, respectively; determining, based on prior aesthetical evaluation data, an aesthetical value of each regional proposal image; and selecting, based on the determined aesthetical value of each regional proposal image, one of the regional proposal images as the target image; extracting, from the AI engine, the target image; and causing the target image to be displayed via a display of a user device.