Learning Transformer Features for Image Quality Assessment

Abstract

Objective image quality evaluation is a challenging task, which aims to measure the quality of a given image automatically. According to the availability of the reference images, there are Full-Reference and No-Reference IQA tasks, respectively. Most deep learning approaches use regression from deep features extracted by Convolutional Neural Networks. For the FR task, another option is conducting a statistical comparison on deep features. For all these methods, non-local information is usually neglected. In addition, the relationship between FR and NR tasks is less explored. Motivated by the recent success of transformers in modeling contextual information, we propose a unified IQA framework that utilizes CNN backbone and transformer encoder to extract features. The proposed framework is compatible with both FR and NR modes and allows for a joint training scheme. Evaluation experiments on three standard IQA datasets, i.e., LIVE, CSIQ and TID2013, and KONIQ-10K, show that our proposed model can achieve state-of-the-art FR performance. In addition, comparable NR performance is achieved in extensive experiments, and the results show that the NR performance can be leveraged by the joint training scheme.