16 September 1996 Measurements of JND property of HVS and its applications to image segmentation, coding, and requantization
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Abstract
In this paper, we measure the gray level JND (just noticeable difference) property of human visual system directly under various viewing conditions. We then developed three image processing tasks using the measured JND data. First, a JND based image segmentation algorithm for coding purpose is proposed. The algorithm operates on a pyramid data structure and uses the JND property as the merge criterion which is simple in computation while proven to be effective and robust in segmenting various images such as Lena, Salesman, etc. Second, the blocky artifacts normally seen in segmented images can be improved by encoding the difference image between the original image and its segmented version. With slight modifications, the JND based segmentation algorithm can effectively segment the difference image for the proposed two-pass progressive image coding. Finally, the measured JND data shows that 55 gray levels per pixel are sufficient to represent an image under normal viewing conditions and that 64 gray levels are sufficient under any viewing condition. An image requantization algorithm is then proposed and its effectiveness verified.
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Day-Fann Shen, Day-Fann Shen, Shih-Chang Wang, Shih-Chang Wang, } "Measurements of JND property of HVS and its applications to image segmentation, coding, and requantization", Proc. SPIE 2952, Digital Compression Technologies and Systems for Video Communications, (16 September 1996); doi: 10.1117/12.251269; https://doi.org/10.1117/12.251269
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