Deep Objective Image Quality Assessment

Abstract

We present a generic blind image quality assessment method that is able to detect common operations that affect image quality as well as estimate parameters of these operations (e.g. JPEG compression quality). For this purpose, we propose a CNN architecture for multi-label classification and integrate patch predictions to obtain continuous parameter estimates. We train this architecture using softmax layers that support multi-label classification and simultaneous training on multiple datasets with heterogeneous labels. Experimental results show that the resulting multi-label CNNs perform similarly to multiple individually trained CNNs while being several times more efficient, and that common image operations and their parameters can be estimated with high accuracy. Furthermore, we demonstrate that the learned features are discriminative for subjective image quality assessment, achieving state-of-the-art results on the LIVE2 dataset via transfer learning. The proposed CNN architecture supports any multi-label classification problem.