MCL-JCV: A JND-based H.264/AVC video quality assessment dataset

A compressed video quality assessment dataset based on the just noticeable difference (JND) model, called MCL-JCV, is recently constructed and released. In this work, we explain its design objectives, selected video content and subject test procedures. Then, we conduct statistical analysis on collec...

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Bibliographic Details
Published inProceedings - International Conference on Image Processing pp. 1509 - 1513
Main Authors Haiqiang Wang, Weihao Gan, Sudeng Hu, Lin, Joe Yuchieh, Lina Jin, Longguang Song, Ping Wang, Katsavounidis, Ioannis, Aaron, Anne, Kuo, C.-C Jay
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Gaussian Mixture Model
Just Noticeable Difference
Measurement
Nonlinear distortion
Outlier Detection
Quality assessment
Video coding
Video Quality Assessment
Video sequences
Visualization
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Summary:A compressed video quality assessment dataset based on the just noticeable difference (JND) model, called MCL-JCV, is recently constructed and released. In this work, we explain its design objectives, selected video content and subject test procedures. Then, we conduct statistical analysis on collected JND data. We compute the difference between every two adjacent JND points and propose an outlier detection algorithm to remove unreliable data. We also show that each JND difference group can be well approximated by a normal distribution so that we can adopt the Gaussian mixture model (GMM) to characterize the distribution of multiple JND points. Finally, it is demonstrated by experimental results that the proposed JND analysis performed in the difference domain, called the D-method, achieves a lower BIC (Bayesian information criteria) value than the previously proposed G-method.
ISSN:2381-8549
DOI:10.1109/ICIP.2016.7532610