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This paper presents a method to discriminate pixel differences according to their impact toward perceived visual quality. Noticeable local contrast changes are formulated firstly since contrast is the basic sensory feature in the human visual system (HVS) perception. The analysis aims at quantifying the actual impact of such changes (further divided into increases and decreases on edges) in different signal contexts. An associated full-reference distortion metric proposed next provides better match with the HVS viewing. Experiments have used two independent visual data sets and the related subjective viewing results, and demonstrated the performance improvement of the proposed metric over the relevant existing ones with various video/images and under diversified test conditions. The proposed metric is particularly effective to visual signal with blurring and luminance fluctuations as the major artifacts, and brings about the fundamental improvement when sharpened image edges are involved.