Blind Image Quality Measurement by Exploiting High-Order Statistics With Deep Dictionary Encoding Network
Qiuping Jiang, Wei Gao, Shiqi Wang, Guanghui Yue, Feng Shao, Yo‐Sung Ho, Sam Kwong
Abstract
Blind image quality measurement (BIQM) has achieved great progress due to the deployment of deep neural networks (DNNs) for training end-to-end models. Most of the existing DNN-based BIQM methods simply aggregate the local deep feature maps with a global max or average pooling layer to generate holistic feature vectors for quality prediction. However, such pooling strategy fails to capture the high-order statistics of local feature descriptors. Inspired by the success of dictionary encoding-based BIQM methods, this article proposes a deep dictionary encoding network (Deep-DEN) that can well capture the high-order statistics of local deep features in an end-to-end manner. In Deep-DEN, dictionary encoding is encapsulated into a single learnable layer attached to the end of a backbone network and followed by a fully connected layer for quality prediction. As a result, high-order statistics of the extracted local deep features in the backbone network and quality prediction functions are simultaneously optimized in a fully supervised manner. The performance of Deep-DEN has been extensively evaluated on several benchmarks and the superiority has been well validated by comparisons with other state-of-the-art BIQM methods.