论文标题
使用多方法融合的成对图像到图像翻译质量评估
Paired Image-to-Image Translation Quality Assessment Using Multi-Method Fusion
论文作者
论文摘要
如何最好地评估合成图像一直是图像到图像翻译的一个长期问题，迄今为止，基本上仍未解决。本文提出了一种新颖的方法，将图像质量的信号结合在一起，在配对源和转换之间，以预测后者的相似性与假设的基础真理。我们使用图像质量评估（IQA）指标通过梯度增强回归器的集合来培训了多方法融合（MMF）模型，以预测深层的图像结构和纹理相似性（DIST），从而使模型在无需地面真实数据的情况下可以排名。分析揭示了要受到特征受限的任务，并在公制计算时间和预测准确性之间引入了权衡。我们提出的MMF模型提供了一种有效的方法来自动化合成图像的评估，并通过扩展生成它们的图像到图像翻译模型。
How best to evaluate synthesized images has been a longstanding problem in image-to-image translation, and to date remains largely unresolved. This paper proposes a novel approach that combines signals of image quality between paired source and transformation to predict the latter's similarity with a hypothetical ground truth. We trained a Multi-Method Fusion (MMF) model via an ensemble of gradient-boosted regressors using Image Quality Assessment (IQA) metrics to predict Deep Image Structure and Texture Similarity (DISTS), enabling models to be ranked without the need for ground truth data. Analysis revealed the task to be feature-constrained, introducing a trade-off at inference between metric computation time and prediction accuracy. The MMF model we present offers an efficient way to automate the evaluation of synthesized images, and by extension the image-to-image translation models that generated them.
