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.