Paired Image-to-Image Translation Quality Assessment Using Multi-Method Fusion

TL;DR

This paper introduces a Multi-Method Fusion model that combines various image quality metrics to predict the similarity of synthesized images to ground truth, enabling effective evaluation without needing actual ground truth images.

Contribution

It presents a novel ensemble approach that leverages multiple IQA metrics to assess image translation quality, addressing the challenge of evaluation without ground truth.

Findings

The MMF model accurately predicts image similarity scores.

Trade-offs exist between computation time and prediction accuracy.

The approach automates image quality assessment in image-to-image translation.

Abstract

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.