In this paper, we introduce a visual pattern degradation based full-reference (FR) image quality assessment (IQA) method. Researches on visual recognition indicate that the human visual system (HVS) is highly adaptive to extract visual structures for scene understanding. Existing structure degradation based IQA methods mainly take local luminance contrast to represent structure, and measure quality as degradation on luminance contrast. In this paper, we suggest that structure includes not only luminance contrast but also orientation information. Therefore, we analyze the orientation characteristic for structure description. Inspired by the orientation selectivity mechanism in the primary visual cortex, we introduce a novel visual pattern to represent the structure of a local region. Then, the quality is measured as the degradations on both luminance contrast and visual pattern. Experimental results on Five benchmark databases demonstrate that the proposed visual pattern can effectively represent visual structure and the proposed IQA method performs better than the existing IQA metrics.
Local structure, e.g., local binary pattern (LBP), is widely used in texture classification. However, LBP is too
sensitive to disturbance. In this paper, we introduce a novel structure for texture classification. Researches
on cognitive neuroscience indicate that the primary visual cortex presents remarkable orientation selectivity for
visual information extraction. Inspired by this, we investigate the orientation similarities among neighbor pixels,
and propose an orientation selectivity based pattern for local structure description. Experimental results on
texture classification demonstrate that the proposed structure descriptor is quite robust to disturbance.