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Proceedings Paper

Experimental design and analysis of JND test on coded image/video
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Paper Abstract

The visual Just-Noticeable-Difference (JND) metric is characterized by the detectable minimum amount of two visual stimuli. Conducting the subjective JND test is a labor-intensive task. In this work, we present a novel interactive method in performing the visual JND test on compressed image/video. JND has been used to enhance perceptual visual quality in the context of image/video compression. Given a set of coding parameters, a JND test is designed to determine the distinguishable quality level against a reference image/video, which is called the anchor. The JND metric can be used to save coding bitrates by exploiting the special characteristics of the human visual system. The proposed JND test is conducted using a binary-forced choice, which is often adopted to discriminate the difference in perception in a psychophysical experiment. The assessors are asked to compare coded image/video pairs and determine whether they are of the same quality or not. A bisection procedure is designed to find the JND locations so as to reduce the required number of comparisons over a wide range of bitrates. We will demonstrate the efficiency of the proposed JND test, report experimental results on the image and video JND tests.

Paper Details

Date Published: 22 September 2015
PDF: 11 pages
Proc. SPIE 9599, Applications of Digital Image Processing XXXVIII, 95990Z (22 September 2015); doi: 10.1117/12.2188389
Show Author Affiliations
Joe Yuchieh Lin, Univ. of Southern California (United States)
Lina Jin, Univ. of Southern California (United States)
Sudeng Hu, Univ. of Southern California (United States)
Ioannis Katsavounidis, Netflix, Inc. (United States)
Zhi Li, Netflix, Inc. (United States)
Anne Aaron, Netflix, Inc. (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 9599:
Applications of Digital Image Processing XXXVIII
Andrew G. Tescher, Editor(s)

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