Laser & Optoelectronics Progress, Volume. 60, Issue 4, 0410023(2023)

No-Reference Image Quality Assessment Algorithm Based on Semi-Supervised Learning

Xiangdong Jin and Qingbing Sang*
Author Affiliations
  • School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, Jiangsu, China
  • show less
    References(33)

    [1] Kim J, Lee S. Deep learning of human visual sensitivity in image quality assessment framework[C], 1969-1977(2017).

    [2] Ma Q, Li T, Zhao J F et al. Infrared image enhancement using visual saliency analysis within local window[J]. Optical Technique, 47, 601-607(2021).

    [3] Lu P, Liu K Y, Zou G L et al. No reference image quality assessment based on fusion of multiple features and convolutional neural network[J]. Chinese Journal of Liquid Crystals and Displays, 37, 66-76(2022).

    [4] Ye M M, Hu J B, Wang X J et al. No-reference stereoscopic image quality assessment based on binocular neuron response[J]. Laser & Optoelectronics Progress, 58, 2410007(2021).

    [5] Shen F P, Zhu T, Zhang H N et al. Non-reference blur image quality evaluation based on saliency object classification[J]. Laser & Optoelectronics Progress, 58, 2210015(2021).

    [6] Kang L, Ye P, Li Y et al. Convolutional neural networks for no-reference image quality assessment[C], 1733-1740(2014).

    [7] Ma K D, Liu W T, Zhang K et al. End-to-end blind image quality assessment using deep neural networks[J]. IEEE Transactions on Image Processing, 27, 1202-1213(2018).

    [8] Liu X L, van de Weijer J, Bagdanov A D. RankIQA: learning from rankings for no-reference image quality assessment[C], 1040-1049(2017).

    [9] Ye P, Kumar J, Doermann D. Beyond human opinion scores: blind image quality assessment based on synthetic scores[C], 4241-4248(2014).

    [10] Zhang W X, Ma K D, Yan J et al. Blind image quality assessment using a deep bilinear convolutional neural network[J]. IEEE Transactions on Circuits and Systems for Video Technology, 30, 36-47(2020).

    [12] Li D Q, Jiang T T, Lin W S et al. Which has better visual quality: the clear blue sky or a blurry animal?[J]. IEEE Transactions on Multimedia, 21, 1221-1234(2019).

    [13] Zhang W X, Ma K D, Zhai G T et al. Learning to blindly assess image quality in the laboratory and wild[C], 111-115(2020).

    [14] Ding X H, Zhang X Y, Ma N N et al. RepVGG: making VGG-style ConvNets great again[C], 13728-13737(2021).

    [15] He K M, Zhang X Y, Ren S Q et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C], 1026-1034(2015).

    [16] Sheikh H R, Sabir M F, Bovik A C. A statistical evaluation of recent full reference image quality assessment algorithms[J]. IEEE Transactions on Image Processing, 15, 3440-3451(2006).

    [17] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 13, 600-612(2004).

    [18] Zhang L, Zhang L, Mou X Q et al. FSIM: a feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 20, 2378-2386(2011).

    [19] Zhang L, Shen Y, Li H Y. VSI: a visual saliency-induced index for perceptual image quality assessment[J]. IEEE Transactions on Image Processing, 23, 4270-4281(2014).

    [20] Xue W F, Zhang L, Mou X Q et al. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index[J]. IEEE Transactions on Image Processing, 23, 684-695(2014).

    [21] Sun W, Liao Q M, Xue J H et al. SPSIM: a superpixel-based similarity index for full-reference image quality assessment[J]. IEEE Transactions on Image Processing, 27, 4232-4244(2018).

    [22] Ponomarenko N, Lukin V, Zelensky A et al. TID2008:a database for evaluation of full-reference visual quality assessment metrics[J]. Advances of Modern Radioelectronics, 10, 30-45(2009).

    [23] Ponomarenko N, Jin L N, Ieremeiev O et al. Image database TID2013: peculiarities, results and perspectives[J]. Signal Processing: Image Communication, 30, 57-77(2015).

    [24] Larson E C, Chandler D M. Most apparent distortion: full-reference image quality assessment and the role of strategy[J]. Journal of Electronic Imaging, 19, 011006(2010).

    [25] Ghadiyaram D, Bovik A C. Massive online crowdsourced study of subjective and objective picture quality[J]. IEEE Transactions on Image Processing, 25, 372-387(2016).

    [26] Hosu V, Lin H H, Sziranyi T et al. KonIQ-10k: an ecologically valid database for deep learning of blind image quality assessment[J]. IEEE Transactions on Image Processing, 29, 4041-4056(2020).

    [27] Bahrami K, Kot A C. A fast approach for no-reference image sharpness assessment based on maximum local variation[J]. IEEE Signal Processing Letters, 21, 751-755(2014).

    [28] Chen D Q, Wang Y Z, Gao W. No-reference image quality assessment: an attention driven approach[J]. IEEE Transactions on Image Processing, 29, 6496-6506(2020).

    [29] Li L D, Xia W H, Lin W S et al. No-reference and robust image sharpness evaluation based on multiscale spatial and spectral features[J]. IEEE Transactions on Multimedia, 19, 1030-1040(2017).

    [30] Li H Y, Zhu F, Qiu J H. CG-DIQA: no-reference document image quality assessment based on character gradient[C], 3622-3626(2018).

    [31] Xu J T, Ye P, Li Q H et al. Blind image quality assessment based on high order statistics aggregation[J]. IEEE Transactions on Image Processing, 25, 4444-4457(2016).

    [32] Bosse S, Maniry D, Müller K R et al. Deep neural networks for no-reference and full-reference image quality assessment[J]. IEEE Transactions on Image Processing, 27, 206-219(2018).

    Tools

    Get Citation

    Copy Citation Text

    Xiangdong Jin, Qingbing Sang. No-Reference Image Quality Assessment Algorithm Based on Semi-Supervised Learning[J]. Laser & Optoelectronics Progress, 2023, 60(4): 0410023

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image Processing

    Received: Jan. 14, 2022

    Accepted: Mar. 30, 2022

    Published Online: Feb. 14, 2023

    The Author Email: Sang Qingbing (sangqb@163.com)

    DOI:10.3788/LOP220543

    Topics