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.
Abstract (translated)
近年来，深度生成模型取得了显著进展，能够仅凭简短的文本提示或参考图像就生成高质量的图片。然而，最先进的技术尚未达到传统摄影方法所要求的质量标准。因此，在使用生成图像的工作流程中，通常会包含一个手动质量评估（IQA）阶段。这一过程既耗时又昂贵，尤其是由于自动产生的图像中只有很少一部分能通过质量审核。 可以通过引入自动预过滤阶段来减轻IQA工作量。这种预过滤可以提高提交审查的图片整体质量，并因此降低获得高质量图像所需的平均成本。我们提出了一种根据通用IQA引擎的精度和合格率估算节省成本的公式。该公式的应用案例为背景修复，其中通过一个简单的自动机器学习（AutoML）解决方案实现了51.61%的成本节约。 这种方法不仅能够显著减少手动审查工作量，还能提高生成图像的质量，从而优化整个生产流程的效率与效果。
URL
https://arxiv.org/abs/2510.16179
