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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 vectordoi:10.1049/el.2017.0900 fatcat:bkfaserxdfaxvbrxlsen6722pq