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No-ReferenceColor Image Quality Assessment Using HOSVD Based Features and Neural Networks

Images and videos are seen as the most reliable source of visual information as they are a fundamental part of the multimedia world. For instance, face recognition technology utilizes images for security purposes. However, due to either the physical properties of the acquisition equipment (internal) or the nature of the environment (external), images can be affected by a wide range of distortions. Researchers have enumerated more than 24 distortions that can affect images. Among which four types are the most prominent ones. In this paper, a novel no-reference color image quality assessment technique is introduced. The technique is based on extracting a set of features from the High Order Singular Value Decomposition (HOSVD) of images. Such features are then used with a neural network regressor to predict the quality score. The results show excellent performance exceeding traditional techniques based only on gray-scale images.