Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154467
Title: Blind night-time image quality assessment : subjective and objective approaches
Authors: Xiang, Tao
Yang, Ying
Guo, Shangwei
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Xiang, T., Yang, Y. & Guo, S. (2020). Blind night-time image quality assessment : subjective and objective approaches. IEEE Transactions On Multimedia, 22(5), 1259-1272. https://dx.doi.org/10.1109/TMM.2019.2938612
Journal: IEEE Transactions on Multimedia
Abstract: Blind image quality assessment (BIQA) aims to develop quantitative measures to automatically and accurately estimate the visual quality of an image without any prior information about its reference image. This issue has been attracting a great deal of attention for a long time; however, little work has been done on night-time images, which are crucially important for consumer photography and practical applications such as automated driving systems. In this paper, to the best of our knowledge, we conduct the first exploration on subjective and objective quality assessment of night-time images. First, we build a large-scale natural night-time image database (NNID) containing 2240 images with 448 different image contents captured by different photographic equipment in real-world scenarios. Subsequently, we carry out a subjective experiment to evaluate the perceptual quality of all the images in the NNID database. Thereafter, we perform objective assessment of night-time images by proposing a blind night-time image quality assessment metric using brightness and texture features (BNBT). Finally, extensive experiments are conducted to evaluate the performance and efficiency of the proposed BNBT metric on the NNID database. The experimental results demonstrate that this metric outperforms existing state-of-the-art BIQA methods in terms of all evaluation criteria and has an acceptable computational cost at the same time. We have made the NNID database publicly available for downloading at https://sites.google.com/site/xiangtaooo/.
URI: https://hdl.handle.net/10356/154467
ISSN: 1520-9210
DOI: 10.1109/TMM.2019.2938612
Rights: © 2019 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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