A Global Reduced Reference Image Quality Metric (IQM)
based on feature fusion using neural networks is proposed.
The main idea is the introduction of a Reduced Reference
degradation-dependent IQM (RRIQM/D) across a set of
common distortions. The first stage consists of extracting a
set of features from the wavelet-based edge map. Such features
are then used to identify the type of degradation using
Linear Discriminant Analysis (LDA). The second stage consists
of fusing the extracted features into a single measure
using Artificial Neural Networks (ANN). The result is a degradation-
dependent IQM measure called the RRIQM/D. The
performance of the proposed method is evaluated using the
TID 2008 database and compared to some existing IQMs.
The experimental results obtained using the proposed
method demonstrate an improved performance even when
compared to some Full Reference IQMs.