Image quality assessment using a SVD-based structural projection

• Singular value decomposition is regarded as a structural projection transform on a set of under-complete bases.
• Image degradation can result in the changes of the projection bases and the coefficients of this structural projection.
• We use a block structural projection to detect the local image distortion feature vectors.
• Both perceptual spatial pooling and neural networks are employed to combine feature vectors to a single quality score.

The development of objective image quality assessment (IQA) metrics aligned with human perception is of fundamental importance to numerous image-processing applications. Recently, human visual system (HVS)-based engineering algorithms have received widespread attention for their low computational complexity and good performance. In this paper, we propose a new IQA model by incorporating these available engineering principles. A local singular value decomposition (SVD) is first utilised as a structural projection tool to select local image distortion features, and then, both perceptual spatial pooling and neural networks (NN) are employed to combine feature vectors to predict a single perceptual quality score. Extensive experiments and cross-validations conducted with three publicly available IQA databases demonstrate the accuracy, consistency, robustness, and stability of the proposed approach compared to state-of-the-art IQA methods, such as Visual Information Fidelity (VIF), Visual Signal to Noise Ratio (VSNR), and Structural Similarity Index (SSIM).