AI Image Realism Evaluation Using Object Segmentation and Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing generative AI systems lack the ability to objectively assess the realism of generated images, leading to potential discrepancies and loss of credibility when used for critical purposes.
Innovation Solution
A system and method that uses deep learning-based instance segmentation and Hu moment-based characteristics to classify and compare objects in digital images, providing a quantitative realism score to evaluate and rank images based on their alignment with real-world content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI models are used to synthesize digital images from text prompts, then image generation capability is improved, but the accuracy and realism of the generated images deteriorate
Solution Approach 1:
The patent introduces an intermediary evaluation system that acts as a mediator between the generative AI model and the user. This system includes an object detector that identifies objects in generated images, a reference image searcher that finds real-world counterparts, and a similarity evaluator that quantifies realism. This intermediary chain enables objective assessment of image realism without requiring users to have expert knowledge, thereby resolving the contradiction between generation capability and realism accuracy.
2Measurement precision
If users manually evaluate the realism of AI-generated images, then image quality assessment is possible, but the time and effort required increases
Solution Approach 1:
The system implements self-service evaluation where the evaluation process is automated and does not require manual user intervention. The object detector automatically identifies objects, the search engine autonomously finds reference images, and the similarity evaluator automatically computes realism scores. This self-service mechanism enables rapid, scalable evaluation of multiple generated images without consuming user time, resolving the contradiction between assessment precision and time investment.
3Measurement precision
If detailed object comparison is performed to assess image realism, then evaluation accuracy is improved, but the system complexity increases
Solution Approach 1:
The evaluation system is segmented into three independent, modular components: an object detector module that identifies objects in images, a reference image searcher module that retrieves real-world reference images, and a similarity evaluator module that computes realism scores. Each module performs a specific function and can be independently developed, tested, and optimized. This segmentation reduces overall system complexity while maintaining high evaluation accuracy, as each component can be kept relatively simple yet effective in its specialized task.
Data Source
AI summary
Described herein are systems, methods, devices, and other techniques for comprehensive and automated evaluation of digital images generated from artificial intelligence (AI) models in order to promote accurate representations of real-world content. Prompts are received at the system that are then passed to both a search engine and a generative AI model. Synthesized digital images are obtained from the generative AI model. The top-matching image from the search engine is used as a verification of the ground truth of the synthesized digital images. A realism score is generated for each synthesized digital image that characterizes the accuracy of the synthesized digital image with reference to the verification image. The realism score can be used to assist and expedite the image selection process, as well as serve as input to fine-tune the performance of generative models.