A no‐reference blurred colourful image quality assessment method based on dual maximum local information
Abstract Images can be blurred due to the imperfection of the imaging system and blurriness is one of the challenging problems for image quality assessment (IQA). No‐reference blurred IQA methods have been proposed in the literature to calculate image blurriness. Inspired by image processing‐based a...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Wiley
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/99bd0b5e6f444e649c95acd51c97d116 |
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| Sumario: | Abstract Images can be blurred due to the imperfection of the imaging system and blurriness is one of the challenging problems for image quality assessment (IQA). No‐reference blurred IQA methods have been proposed in the literature to calculate image blurriness. Inspired by image processing‐based auto‐focussing and maximum local information theories, a no‐reference blurred colourful IQA method based on Dual Maximum Local Information is proposed here. First, a window extraction method that combines the maximum gradient with local entropy is proposed to obtain the region of interest (ROI) for subsequent processing. Second, an improved maximum gradient method that leverages information from different channel images is presented to calculate the maximum gradient variation within the ROI for final sharpness score. Experimental results illustrated that the proposed method has better performance under various measurements compared with the state‐of‐the‐art methods on LIVE, CSIQ, TID2008, TID2013, VCL@FER, IVC image databases. |
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