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Image quality assessment using a SVD-based structural projection
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AI
DOI：10.1016/j.image.2014.01.007.png)
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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). (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Image quality assessment (IQA)
Human visual system (HVS)
Singular value decomposition (SVD)
Structural projection
Neural networks (NN)
Spatial pooling
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2.7
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2.8K
被引数:
4.2K
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