Cost Savings from Automatic Quality Assessment of Generated Images

2025-10-17

AI Summary

High manual image quality assessment (IQA) costs and low efficiency arise from the prevalence of substandard generated images in industrial workflows. Method: This paper proposes an automated pre-screening mechanism tailored for industrial pipelines, built upon an AutoML-driven lightweight image quality assessment engine that jointly optimizes pass rate and accuracy to enable automatic filtering and prioritization of candidate images. It further introduces the first mathematical model quantifying cost savings achieved by pre-screening systems. Contribution/Results: Evaluated in a background restoration scenario, the approach reduces manual review costs by 51.61% while significantly improving the throughput of compliant images. This work establishes a deployable, automated paradigm for generative image quality control and pioneers a quantitative framework for joint quality–cost optimization in generative AI applications.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

Deep generative models have shown impressive progress in recent years, making it possible to produce high quality images with a simple text prompt or a reference image. However, state of the art technology does not yet meet the quality standards offered by traditional photographic methods. For this reason, production pipelines that use generated images often include a manual stage of image quality assessment (IQA). This process is slow and expensive, especially because of the low yield of automatically generated images that pass the quality bar. The IQA workload can be reduced by introducing an automatic pre-filtering stage, that will increase the overall quality of the images sent to review and, therefore, reduce the average cost required to obtain a high quality image. We present a formula that estimates the cost savings depending on the precision and pass yield of a generic IQA engine. This formula is applied in a use case of background inpainting, showcasing a significant cost saving of 51.61% obtained with a simple AutoML solution.

Problem

• Automating quality assessment for generated images to reduce costs
• Replacing manual image review with automatic pre-filtering systems
• Estimating cost savings from improved image quality evaluation methods

Innovation

• Automated pre-filtering for image quality assessment
• Cost-saving formula based on precision and yield
• AutoML solution achieving 51.61% cost reduction