Abstract
Most of the existing image quality assessment (IQA) methods are directed to artificially synthesized distorted images, in which the types and characteristics of distortion are different from those in the real world. In view of the fact that the existing non-reference IQA methods can not accurately evaluate the quality of the real distortion image, combined with the theoretical analysis of multi-layer visual perception mechanism, we propose a real image distortion IQA method based on image underlying features and high-level semantics. Considering non-linear hierarchical structure of human visual perception, firstly, k-means clustering algorithm is performed according to the underlying feature indexs of the image so that the used image database can be divided into several groups, which aims to improve the accuracy of predicted quality score. Secondly, the deep convolutional neural network (DCNN) is used to extract the first-grade high-level semantic features in each group. Then, second-grade high-level semantic features that can provide better representation of image features are obtained by performing multiple statistical functions on first-grade high-level semantics. Besides, we establish an effective high-capacity regressor with high-level semantics and subjective mean opinion scores (MOS) values of the human eyes. The experimental results show that the proposed model on the KonIQ-10 k image database can predict the quality score effectively and achieve a high consistency with the corresponding MOS value, which is helpful for the subsequent image enhancement.
Similar content being viewed by others
References
Bianco S, Celona L, Napoletano P, Schettini R (2016) On the use of deep learning for blind image quality assessment. Signal Image & Video Processing 3:1–8
Bosse S, Maniry D, Wiegand T, Samek W (2016) A deep neural network for image quality assessment. IEEE International Conference on Image Processing.
Cai W, Wei Z (2020) PiiGAN: generative adversarial networks for pluralistic image Inpainting. IEEE Access 8:48451–48463
Dosovitskiy A, Brox T Generating images with perceptual similarity metrics based on deep networks. NIPS
Fan DP, Cheng MM, Liu Y, et al (2017) Structure-measure: a new way to evaluate foreground maps[C] // IEEE international conference on computer vision
Fan DP, Gong C, Cao Y, Ren B, Cheng MM, Borji A (2018) Enhanced-alignment measure for binary fore-ground map evaluation[C]// computer vision and. Pattern Recognition
Fan DP, Zhang SC, Wu YH, et al (2019) Scoot: A perceptual metric for facial sketches[J]. Proceedings/ IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision
Gao F, Wang Y, Li P, Tan M, Yu J, Zhu Y (2017) Deepsim: deep similarity for image quality assessment. Neuro-computing.:S092523121–S097301480
Ghadiyaram D, Bovik AC (2015) Feature maps driven no-reference image quality prediction of authentically distorted images. Proceedings of SPIE - The International Society for Optical Engineering. https://doi.org/10.1117/12.2084807
Hawkins J (2006) The future of AI[M]. Shaanxi Science and Technology Press, Shanxi
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, 770–778. Computer Vision and Pattern Recognition, 2015. https://doi.org/10.1109/CVPR.2016.90
He N, Xie K, Li T (2017) A review of image quality evaluation. Journal of Beijing Institute of Graphic Communication 25(02):47–50
Jia Y.e.a. (2014) Caffe: Convolutional architecture for fast feature embedding. In: ACM MM.; pp. 675–678.
Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. ECCV
Kang L, Ye P, Li Y, Doermann D (1733–1740) Convolutional neural networks for no-reference image quality assessment. In CVPR 2014
Kim J, Lee S (2017) Fully deep blind image quality predictor. IEEE Journal of Selected Topics in Signal Processing 11(1):206–220
Li D, Jiang T, Jiang M (2017) Exploiting high-level semantics for no-reference image quality assessment of realistic blur images. Proceedings of the 2017 ACM on Multimedia Conference 2017. https://doi.org/10.1145/3123266.3123322.
Lin H, Hosu V, Saupe, D (2018) “KonIQ-10k: Towards an ecologically valid and large-scale IQA database,” Computer Vision and Pattern Recognition.
Liu L, Liu B, Huang H, Bovik AC (2014) No-reference image quality assessment based on spatial and spectral entropies. Signal Process Image Commun 29(8):856–863
Liu X, Wang X, Zhang T (2015) An image quality evaluation method based on visual underlying features. Optical. 41(03):280–284
Lu P, Lin G, Zou G (2018) Study on non-reference image quality evaluation method based on information entropy and deep learning. Application Research of Computer 35(11):3508–3512
Mansouri A, Mahmoudi-Aznaveh A (2019) SSVD: Struc-tural SVD-based image quality assessment. Image Communication, Signal Processing
Margolin R, Zelnik-Manor L, Tal A (2014) How to evaluate foreground maps[C]// IEEE conference on computer vision and pattern. IEEE
Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society. 21(12):4695–4708
Moorthy K, Bovik, Conrad A (2011) Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans Image Process 20(12):3350–3364
Saad MA, Bovik AC, Charrier C (2012) Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 21(8):3339.0
Siahaan E, Hanjalic A (2016) A RediJ Augmenting blind image quality assess-ment using image semantics In IEEE International Symposium on Multimedia:307–312
Simonyan K (2014) Zisserman. A. Very deep convolu-tional networks for large-scale image recognition, Computer Science
Sun J, Li H (2017) Automatic image annota-tion based on multi-feature fusion and PLSA-GMM. Measurement & Control Technology 36(04):31–35+39
Sun C, Li H, Li W (2016) No-reference image quality assessment based on global and local content perception. In Visual Communications and Image Processing
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D et al. (2015) Going deeper with convolutions. Computer Vision and Pattern Recognition.; pp. 1–9.
Tang M (2018) Research on real image quality evaluation method based on subjective perception. Beijing University of Posts and Telecommunications
Varga, D. ;Saupe, D. ; Sziranyi, T. Deeprn: (2018) A Content preserving deep architecture for blind image quality assessment. 2018 IEEE International Conference on Multimedia and Expo (ICME)
Wang YX, Hebert M (2016) Learning to learn: model regression networks for easy small sample learning. Computer Vision – ECCV
Yang L (2017) Research on non-reference quality evaluation based on real distortion image. Beijing University of Posts and Telecommunica-tions
Yang L, Du H, Xu J, Yong L (2016) Blind image quality assessment on authentically distorted images with perceptual features. IEEE International Conference on Image Processing
You H, Tian S, Yu L, Lv Y (Feb. 2020) Pixel-level remote sensing image recognition based on bidirectional word vectors. in IEEE Transactions on Geoscience and Remote Sensing 58(2):1281–1293
Yu S, Wu S, Jiang F, Li L, Xie Y, Wang L (2017) A shallow convolutional neural network for blind image sharpness assessment. PLoS One 12(5):e0176632
Zhang R et al. (2018) The unreasonable effectiveness of deep features as a perceptual metric." CVPR 2018.
Acknowledgements
This work was supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Wang, X., Pang, Y. & Ma, X. Real distorted images quality assessment based on multi-layer visual perception mechanism and high-level semantics. Multimed Tools Appl 79, 25905–25920 (2020). https://doi.org/10.1007/s11042-020-09222-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09222-9