Channel Recombination and Projection Network for Blind Image Quality Measurement
Lili Shen, Bo Zhao, Zhaoqing Pan, Bo Peng, Sam Kwong, Jianjun Lei
Abstract
In this paper, a novel channel recombination and projection network-based no-reference image quality assessment (NR-IQA) is proposed, which is termed as CRPNet. The proposed CRPNet is composed of four parts, a feature extractor, a channel recombination strategy, a saliency-guided selective projection, and a channel score weighting. The feature extractor is first utilized to learn patch-level features from patches to address the lack of training samples. The channel recombination strategy is proposed to obtain image-level features by recombining patch-level features in channel dimension, which can solve the mismatch problem between the image score and the patch-level quality. To further increase the image quality prediction accuracy, the saliency-guided selective projection is designed, in which the mapping ratios of fully connected layers are calculated by the saliency priority of patches. Moreover, a channel score weighting is introduced to enhance the image visual quality representation. Experimental results on six image quality assessment datasets, e.g., LIVE, TID2013, CSIQ, LIVE MD, LIVE CH and Waterloo Exploration Database, show that the proposed CRPNet outperforms the state-of-the-art NR-IQA methods, and achieves the superior performance. Code is available at https: //github.com/zhaob10/CRPNet.