Year of Publication


Document Type





Electrical Engineering

First Advisor

Kevin D. Donohue


Recent studies have been done to create models that predict the response of the human visual system (HVS) based on how the HVS processes an image. The most widely known of these models is the Gabor model, since the Gabor patterns closely resemble the receptive filters in the human eye. The work of this thesis examines the use of Symlets to represent the HVS, since Symlets provide the benefit of orthogonality. One major problem with Symlets is that the energy is not stable in respective Symlet channels when the image patterns are translated spatially. This thesis addresses this problem by up sampling Symlets instead of down sampling, and thus creating shift invariant Symlets. This thesis then compares the representation of Gabor versus Symlet approach in predicting the response of the HVS to detecting print defect patterns such as banding and graining. In summary we noticed that Symlet prediction outperforms the Gabor prediction thus Symlets would be a good choice for HVS response prediction. We also concluded that for banding defect periodicity and size are important factors that affect the response of the HVS to the patterns. For graining defects we noticed that size does not greatly affect the response of the HVS to the defect patterns. We introduced our results using two set of performance metrics, the mean and median.