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Publication
Interactive Demonstrations of the Locally Adaptive Fusion For Combining Objective Quality Measures
Book Contribution - Book Chapter Conference Contribution
To automate quality monitoring of multimedia applications,
objective quality measures for images and video content need
to be designed. Objective quality measures that model the
Human Visual System (HVS) have a disappointing performance,
because the HVS is not sufficiently understood. Integrating
machine learning (ML) techniques may increase the performance.
Unfortunately, traditional ML is difficult to interpret.
To this end, we developed the Locally Adaptive Fusion (LAF),
for more flexible and reliable quality predictions. This
manuscript proposes six interactive programs and a website
that demonstrate the effectiveness of LAF, which complement
the technical focus of the corresponding journal paper.
objective quality measures for images and video content need
to be designed. Objective quality measures that model the
Human Visual System (HVS) have a disappointing performance,
because the HVS is not sufficiently understood. Integrating
machine learning (ML) techniques may increase the performance.
Unfortunately, traditional ML is difficult to interpret.
To this end, we developed the Locally Adaptive Fusion (LAF),
for more flexible and reliable quality predictions. This
manuscript proposes six interactive programs and a website
that demonstrate the effectiveness of LAF, which complement
the technical focus of the corresponding journal paper.
Book: 2014 IEEE International Conference on Image Processing (ICIP)
Series: International Conference on Image Processing Proceedings
Pages: 2180-2182
ISBN:978-1-4799-5751-4
Publication year:2014
Keywords:quality assessment, machine learning, locally adaptive fusion
Accessibility:Closed