access icon free Image quality assessment employing joint structure-colour histograms as quality-aware features

Image quality assessment is of fundamental importance for various image processing applications. A novel method is presented in which the joint occurrences of statistical local representation by log-Gabor filters and texture analysis by local tetra patterns and histograms of colour are considered as quality-aware features. Then the dissimilarities of these features between the distorted and reference images are quantified and mapped into quality score prediction by utilising a support vector regression. Extensive experiments on LIVE, CSIQ and TID databases show that the proposed method is remarkably consistent with human perception and outperforms many state-of-the-art methods, and also it is robust across different distortion types and different databases.

Inspec keywords: support vector machines; image colour analysis; regression analysis; feature extraction; image filtering; Gabor filters; image texture

Other keywords: support vector regression; human perception; log-Gabor filters; image quality assessment; joint structure-colour histograms; reference images; statistical local representation; texture analysis; LIVE databases; TID database; quality score prediction; local tetra patterns; CSIQ database; image processing applications; quality-aware features; joint occurrences; distorted images

Subjects: Other topics in statistics; Filtering methods in signal processing; Computer vision and image processing techniques; Other topics in statistics; Image recognition; Knowledge engineering techniques

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