Blind Image Quality Measurement by Exploiting High-Order Statistics with Deep Dictionary Encoding Network

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.