Blind image quality assessment in multiple bandpass and redundancy domains

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Blind image quality assessment (BIQA) aims to automatically evaluate image quality without any prior knowledge of reference images and distortion types. Most of existing BIQA methods use certain probability distribution models to capture the natural scene statistics features of images in bandpass domains which can be viewed as a process of removing redundancy. There also exist methods which apply NSS features in redundancy domain. In this paper, we propose a novel method that employs both bandpass and redundancy domains to acquire the complementary features in multiple color spaces. Furthermore, hierarchical feature extraction strategy is adopted to make the image representation more powerful. Then we stack them as a multi-channel feature maps group, and use Gaussian mixture model to fit them. Finally, Fisher Vectors are used to encode them and a support vector regression model is trained as the quality predictor. Extensive experiments on four commonly evaluated image quality assessment benchmark databases show the proposed method is very competitive against other BIQA methods and has good generalization ability.