Quality Assessment of Remote Sensing Images Based on Deep Learning and Human Visual System

Di Liu; Yingchun Li

doi:10.3788/LOP56.061101

[1] Wang J Q, Zhang L G, Fu T J et al. Sharpness assessment for remote sensing image based on abstracting the edge image of skeleton[J]. Laser & Optoelectronics Progress, 52, 091002(2015).

[2] Xia Y T, Chen Z Z. Quality assessment for remote sensing images: Approaches and applications. [C]∥IEEE International Conference on Systems, Man, and Cybernetics, October, 9-12, 2015, Kowloon, China. New York: IEEE, 1029-1034(2015).

[3] Sheikh H R, Bovik A C. A visual information fidelity approach to video quality assessment. [C]∥International Workshop on Video Processing and Quality Metrics for Consumer Electronics, [S. l. : s. n.], 23-25(2005).

[4] Miao Y, Yi S L, He J F et al. Image quality assessment of feature similarity combined with gradient information[J]. Journal of Image and Graphics, 20, 749-755(2015).

[5] Chandler D M, Hemami S S. VSNR: A wavelet-based visual signal-to-noise ratio for natural images[J]. IEEE Transactions on Image Processing, 16, 2284-2298(2007). http://www.ncbi.nlm.nih.gov/pubmed/17784602

[6] Kim J, Zeng H, Ghadiyaram D et al. Deep convolutional neural models for picture-quality prediction: Challenges and solutions to data-driven image quality assessment[J]. IEEE Signal Processing Magazine, 34, 130-141(2017). http://ieeexplore.ieee.org/document/8103112

[7] Shao Y, Sun F C, Li H B. No-reference remote sensing image quality assessment method using visual properties[J]. Journal of Tsinghua University (Science and Technology), 53, 550-555(2013).

[8] Zhang F Y, Xie W, Lin L Y et al. No-reference remote sensing image quality assessment based on natural scene statistical in wavelet domain[J]. Journal of Electronics & Information Technology, 33, 2742-2747(2011).

[9] Bare B, Li K, Yan B et al. An accurate deep convolutional neural networks model for no-reference image quality assessment. [C]∥IEEE International Conference on Multimedia and Expo, July 10-14, 2017, Hong Kong, China. New York: IEEE, 1356-1361(2017).

[10] Li S M, Chang Y L, Duan Z C. Objective assessment of stereoscopic image comfort based on convolutional neural network[J]. Acta Optica Sinica, 38, 0610003(2018).

[11] Deng J, Dong W, Socher R et al. ImageNet: A large-scale hierarchical image database. [C]∥ IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL, USA. New York: IEEE, 248-255(2009).

[12] Yu W, Yang K Y, Yao H X et al. Exploiting the complementary strengths of multi-layer CNN features for image retrieval[J]. Neurocomputing, 237, 235-241(2017). http://www.sciencedirect.com/science/article/pii/S0925231216314734

[13] Simonyan K. -04-13)[2018-08-06]. https:∥arxiv., org/abs/1409, 1556(2015).

[14] Yosinski J, Clune J, Bengio Y et al. Montreal, Canada,[2018-08-06]. 2014: 3320-3328. https:∥arxiv., org/abs/1411, 1792(2014).

[15] Gao Z N, Yang X M, Gong J M et al. Research on image complexity description methods[J]. Journal of Image and Graphics, 15, 129-135(2010).

[16] Wei Z G, Yuan J H, Cai Y L. The history, status and future of image quality evaluation[J]. Journal of Image and Graphics: Edition A, 3, 386-389(1998).

[17] 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). http://www.ncbi.nlm.nih.gov/pubmed/17076403