Just Noticeable Distortion Profile Inference : A Patch-Level Structural Visibility Learning Approach

Xuelin SHEN, Zhangkai NI, Wenhan YANG, Xinfeng ZHANG, Shiqi WANG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

36 Citations (Scopus)


In this paper, we propose an effective approach to infer the just noticeable distortion (JND) profile based on patch-level structural visibility learning. Instead of pixel-level JND profile estimation, the image patch, which is regarded as the basic processing unit to better correlate with the human perception, can be further decomposed into three conceptually independent components for visibility estimation. In particular, to incorporate the structural degradation into the patch-level JND model, a deep learning-based structural degradation estimation model is trained to approximate the masking of structural visibility. In order to facilitate the learning process, a JND dataset is further established, including 202 pristine images and 7878 distorted images generated by advanced compression algorithms based on the upcoming Versatile Video Coding (VVC) standard. Extensive experimental results further show the superiority of the proposed approach over the state-of-the-art. Our dataset is available at: https://github.com/ShenXuelin-CityU/PWJNDInfer.
Original languageEnglish
Pages (from-to)26-38
JournalIEEE Transactions on Image Processing
Early online date3 Nov 2020
Publication statusPublished - 2021
Externally publishedYes


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