Software Application Images With Foreground Extraction and Quality Filtering
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Solution Overview
Problem
Creating digital assets for software applications that accurately portray their essence is costly and time-consuming, often requiring substantial human involvement and can result in distorted or inappropriate images due to the limitations of unsupervised text-to-image generation models.
Innovation Solution
A method involving a computing device that extracts foreground elements from an original image, generates candidate images using a stable diffusion model, and filters them based on quality scores to associate relevant images with software applications, employing AI models like masking engines and image selectors to ensure appropriateness and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If unsupervised text-to-image generation models are used to create digital assets, then efficiency is improved, but image quality and appropriateness deteriorate due to distorted or unrealistic images
Solution Approach 1:
The patent implements a feedback mechanism where generated images are evaluated by a selector module that provides feedback on image quality and appropriateness. The system uses quality scores and relevance scores to evaluate generated images and feeds this information back to improve the generation process, thereby maintaining high image quality while preserving the efficiency benefits of automated generation.
Solution Approach 2:
The patent introduces an intermediary evaluation system consisting of a selector module that acts as a mediator between the text-to-image generation model and the final output. This intermediary component assesses generated images for quality and appropriateness, filtering out distorted or unrealistic images while allowing the automated generation process to maintain its efficiency advantages.
2Reliability
If human involvement is used to create and customize digital assets, then image quality and appropriateness are improved, but time consumption and cost increase
Solution Approach 1:
The patent implements self-service automation where the system automatically generates, evaluates, and selects digital assets without requiring human intervention. The automated pipeline includes image generation, quality scoring, relevance scoring, and selection, enabling the system to serve itself and eliminate the time consumption associated with manual creation and customization while maintaining high image quality through automated evaluation.
3Adaptability or versatility
If multiple digital assets are created for different keywords, then relevance to search terms is improved, but complexity of the process increases
Solution Approach 1:
The patent creates a universal automated system that handles multiple tasks within a single integrated pipeline. The same system generates images for different keywords, evaluates their quality and relevance, and selects appropriate assets, eliminating the need for separate processes for each keyword and reducing overall process complexity while maintaining high adaptability to different search terms.
Data Source
AI summary
A method for managing images associated with software applications is disclosed. The method implemented by a computing device can include receiving an original image associated with a software application, extracting a foreground element from the original image to produce an extracted foreground element and a modified original image. The method further includes generating multiple candidate images based on the extracted foreground image, the modified original image, and a keyword associated with the original image. Additionally, the method includes generating, based on the keyword, respective quality scores for the multiple candidate images, filtering the multiple candidate images using their respective quality scores to generate one or more filtered candidate images, and associating the at least one of the one or more filtered candidate images with the software application.