用自动质检减少生成图像的人工审核成本，最高省超一半
Cost Savings from Automatic Quality Assessment of Generated Images
- 设计通用公式评估自动质检的节本效果
- 在背景修复场景实现51.61%成本降低
- 适合需大规模生成图像的生产团队
近年来深度生成模型取得显著进展，仅凭文本提示或参考图即可生成高质量图像。然而，当前技术仍未达到传统摄影的质量标准。因此，使用生成图像的生产流程常包含人工图像质量评估（IQA）环节，该环节耗时且昂贵，尤其因自动生成图像通过质量门槛的比例较低。引入自动预筛选阶段可提升送审图像的整体质量，从而降低获取高质量图像的平均成本。本文提出一个公式，用于估算任意IQA引擎在不同精度和通过率下的成本节省效果。该公式应用于背景修复场景，采用简单AutoML方案即实现51.61%的成本节约。
原文摘要 · Abstract (English)
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.
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