2024/04/28 by Simon Raviv, Raviv, Simon, Gal Chechik +1
Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Color Science and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Graphics (cs.GR) #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Neural Networks and Applications #Statistical and Computational Modeling #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2404.18178
openalex publication_date 2024/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Perceptual image quality assessment (IQA) is the task of predicting the visual quality of an image as perceived by a human observer. Current state-of-the-art techniques are based on deep representations trained in discriminative manner. Such representations may ignore visually important features, if they are not predictive of class labels. Recent generative models successfully learn low-dimensional representations using auto-encoding and have been argued to preserve better visual features. Here we leverage existing auto-encoders and propose VAE-QA, a simple and efficient method for predicting image quality in the presence of a full-reference. We evaluate our approach on four standard benchmarks and find that it significantly improves generalization across datasets, has fewer trainable parameters, a smaller memory footprint and faster run time.