NDLI: A JND Profile Based on Hierarchically Selective Attention for Images

Content Provider	IEEE Xplore Digital Library
Author	Dongdong Zhang Lijing Gao Di Zang Yaoru Sun Jiujun Cheng
Copyright Year	2013
Description	Author affiliation: Comput. Sci. Dept., Tongji Univ., Shanghai, China (Dongdong Zhang; Lijing Gao; Di Zang; Yaoru Sun; Jiujun Cheng)
Abstract	Most of the traditional just-noticeable-distortion (JND) models in pixel domain compute the JND threshold by incorporating the spatial luminance adaptation effect and the textures contrast masking effect. Recently, with the rapid development of the computable models of visual attention, researchers started to improve the JND model by considering visual saliency of images, a foveated spatial JND model (FSJND) was proposed by incorporating the traditional visual characteristics and fovea characteristic of human eyes to enhance JND thresholds. However, the thresholds computed by the FSJND model may be overestimated for some high resolution images. In this paper, we proposed a new JND profile in pixel domain, in which a multi-level modulation function is built to reflect the effect of hierarchically selective visual attention on JND thresholds. The contrast masking is also considered in our modulation function to obtain more accurate JND thresholds. Compared with the lasted JND profiles, the proposed model can tolerate more distortion and has much better perceptual quality. The proposed JND model can be easily applied in many areas, such as compression, error protection, and so on.
Starting Page	263
Ending Page	266
File Size	1295515
Page Count	4
File Format	PDF
e-ISBN	9781479921713
DOI	10.1109/ISM.2013.50
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Visualization Adaptation models Image resolution Modulation PSNR JND hierarchically selective attention multi-level saliency visual masking
Content Type	Text
Resource Type	Article

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