A novel NMF-based image quality assessment metric using extreme learning machine

In this paper, we propose a novel image quality assessment (IQA) metric based on nonnegative matrix factorization (NM-F). With nonnegativity and parts-based properties, NMF well demonstrates how human brain learns the parts of objects. This makes NMF distinguished from other feature extraction methods like singular value decomposition (SVD), principal components analysis (PCA), etc. Inspired by this, we adopt NMF to extract image features from reference and distorted images. Extreme learning machine (ELM) [10] is then employed for feature pooling to obtain the overall quality score. Compared with other machine learning techniques such as neural networks and support vector machines (SVMs), ELM provides better generalization performance with much faster learning speed and less human intervene. Experimental results with the TID database demonstrate that the proposed metric achieves better performance in comparison with the relevant state-of-the-art quality metrics and has lower computational complexity.