Abstract
This paper presents a sparse representation based image Quality metric with Adaptive Sub-Dictionaries (QASD). An overcomplete dictionary is first learned using natural images. A reference image block is represented using the overcomplete dictionary, and the used basis vectors are employed to form an undercomplete sub-dictionary. Then the corresponding distorted image block is represented using all the basis vectors in the sub-dictionary. The sparse coefficients are used to generate two feature maps, based on which a local quality map is generated. With the consideration that sparse features are insensitive to weak distortions and image quality is affected by various factors, image gradient, color and luminance are integrated as auxiliary features. Finally, a sparse-feature-based weighting map is proposed to conduct the pooling, producing an overall quality score. Experiments on public image databases demonstrate the advantages of the proposed method.
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This work is supported by National Natural Science Foundation of China (61379143), Fundamental Research Funds for the Central Universities (2015QNA66), and the S&T Program of Xuzhou City (XM13B119).
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Li, L., Cai, H., Zhang, Y., Qian, J. (2015). Sparse Representation Based Image Quality Assessment with Adaptive Sub-dictionary Selection. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9315. Springer, Cham. https://doi.org/10.1007/978-3-319-24078-7_6
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DOI: https://doi.org/10.1007/978-3-319-24078-7_6
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