  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.
