Abstract
In order to detect different images under different distortion conditions, this paper classifies image distortion types and proposes video image distortion detection and classification based on convolutional neural networks. By segmenting the input video image to obtain a small block image, and then using the active learning feature of the convolutional neural network, the positive and negative cases are equalized, the adaptive learning rate is slowed down and the local minimum problem is solved. The type of distortion is mainly predicted by the SoftMax classifier image, and then the video image prediction type obtained by the majority voting rule is used. The objective quality evaluation algorithm based on image distortion type and convolutional neural network is analyzed. Using LIVE (simulation standard image library) and actual monitoring video library, the final accurate results of the two performance tests are not much different. The overall classification accuracy rate is significantly higher than other algorithms. After the positive and negative case equalization and adaptive learning rate are introduced, it is found that the CNN classification accuracy can be significantly improved. The final test results also confirm that this method can actively learn image quality features, and improve the accuracy of video image classification detection. It is applicable to image quality detection of all video image distortion conditions, and has strong practicability and robustness.
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References
Fan, C., Zhang, Y., Feng, L., et al.: No reference image quality assessment based on multi-expert convolutional neural networks. IEEE Access 6, 8934–8943 (2018)
Wang, H., Fu, J., Lin, W., et al.: Image quality assessment based on local linear information and distortion-specific compensation. IEEE Trans. Image Process. 26(2), 915–926 (2017)
Eerola, T., Lensu, L., Kälviäinen, H., et al.: Study of no-reference image quality assessment algorithms on printed images. J. Electron. Imaging 23(6), 061106 (2014)
Visual quality assessment: recent developments, coding applications and future trends. APSIPA Trans. Signal Inf. Process. 2, e4 (2013)
Ma, L.J., Zhao, C.H.: An effective image fusion method based on nonsubsampled contourlet transform and pulse coupled neural network. Adv. Mater. Res. 756–759, 3542–3548 (2013)
Wang, Z., Ma, Y., Cheng, F., et al.: Review of pulse-coupled neural networks. Image Vis. Comput. 28(1), 5–13 (2010)
Zhang, L., Zhang, L., Bovik, A.C.: A feature-enriched completely blind image quality evaluator. IEEE Trans. Image Process. 24(8), 2579–2591 (2015)
Chetouani, A., Beghdadi, A., Deriche, M.: A hybrid system for distortion classification and image quality evaluation. Signal Process. Image Commun. 27(9), 948–960 (2012)
Yong, C., Qiang, F., Feng, S.: Sparse image fidelity evaluation based on wavelet analysis. J. Electron. Inf. Technol. 37, 2055–2061 (2015)
Wang, H.: A new algorithm for integrated image quality measurement based on wavelet transform and human visual system. Proc. SPIE Int. Soc. Opt. Eng. 6034, 60341K-1–60341K-7 (2006)
Chang, C.C., Lin, M.H., Hu, Y.C.: A fast and secure image hiding scheme based on lsb substitution. Int. J. Pattern Recogn. Artif. Intell. 16(04), 399–416 (2002)
Singh, P., Chandler, D.M.: F-MAD: a feature-based extension of the most apparent distortion algorithm for image quality assessment. Proc. SPIE Int. Soc. Opt. Eng. 8653, 86530I (2013)
Mou, X., Imai, F.H., Xiao, F., et al.: SPIE Proceedings [SPIE IS&T/SPIE electronic imaging - San Francisco airport, California, USA (Sunday 23 January 2011)] digital photography VII - Image quality assessment based on edge. Digit. Photogr. VII 7876, 78760N (2011)
Zhu, L.: Image quality evaluation method based on structural similarity. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 6790, pp. 67905L-67905L-10 (2007)
Hassen, R., Wang, Z., Salama, M.M.A.: Image sharpness assessment based on local phase coherence. IEEE Trans. Image Process. 22(7), 2798–2810 (2013)
Dony, R.D., Coblentz, C.L., Nabmias, C., et al.: Compression of digital chest radiographs with a mixture of principal components neural network: evaluation of performance. RadioGraphics 6(6), 1481–1488 (1996)
Oliveira, S.A.F., Alves, S.S.A., Gomes, J.P.P., et al.: A bi-directional evaluation-based approach for image retargeting quality assessment. Comput. Vis. Image Underst. 168, 172–181 (2017). S1077314217302035
Dendi, S.V.R., Dev, C., Kothari, N., et al.: Generating image distortion maps using convolutional autoencoders with application to no reference image quality assessment. IEEE Signal Process. Lett. 26, 1 (2018)
Bin, J., Jiachen, Y., Zhihan, L., et al.: Wearable vision assistance system based on binocular sensors for visually impaired users. IEEE Internet Things J. 6, 1 (2018)
Hui, C., Chaofeng, L.I.: Stereoscopic color image quality assessment via deep convolutional neural network. J. Front. Comput. Sci. Technol. 12, 1315–1322 (2018)
Rehman, A.U, Rahim, R., Nadeem, M.S., et al.: End-to-end Trained CNN Encode-Decoder Networks for Image Steganography (2017)
Ding, Y., Deng, R., Xie, X., et al.: No-reference stereoscopic image quality assessment using convolutional neural network for adaptive feature extraction. IEEE Access 6, 37595–37603 (2018)
Long, M., Ouyang, C., Liu, H., et al.: Image recognition of Camellia oleifera diseases based on convolutional neural network and transfer learning. Nongye Gongcheng Xuebao/Trans. Chin. Soc. Agric. Eng. 34(18), 194–201 (2018)
Li, Y., Liu, D., Li, H., et al.: Convolutional neural network-based block up-sampling for intra frame coding. IEEE Trans. Circuits Syst. Video Technol. 28(9), 2316–2330 (2017)
Jia, C., Wang, S., Zhang, X., et al.: Content-aware convolutional neural network for in-loop filtering in high efficiency video coding. IEEE Trans. Image Process. 28, 3343–3356 (2019)
Ahn, N., Kang, B., Sohn, K.A.: Image distortion detection using convolutional neural network (2018)
Jongyoo, K., Anh-Duc, N., Sanghoon, L.: Deep CNN-based blind image quality predictor. IEEE Trans. Neural Netw. Learn. Syst. 30, 1–14 (2018)
Mngenge N A . An adaptive quality-based fingerprints matching using feature level 2 (minutiae) and extended features (pores) (2013). Nelwamondo F.v.prof
Miao, Z., Xu, H., Chen, Y., et al.: An Intelligent computational algorithm based on neural network for spatial data mining in adaptability evaluation. Proc. SPIE Int. Soc. Opt. Eng. 7146, 71461 (2009)
Yuan, C.H., Zhang, M., Gao, S.W., et al.: Research on method of state evaluation and fault analysis of dry-type power transformer based on self-organizing neural network. Appl. Mech. Mater. 303–306, 562–566 (2013)
Acknowledgement
This work was jointly supported by the Key Research and Development Project of Gan-zhou, the name is “Research and Application of Key Technologies of License Plate Recognition and Parking Space Guidance in Intelligent Parking Lot”, the Education Department of Jiangxi Province of China Science and Technology research projects with the Grant No. GJJ181265.
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Deng, L., Gu, F., Xie, S. (2020). Research on Intelligent Algorithm for Image Quality Evaluation Based on Image Distortion Type and Convolutional Neural Network. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_61
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DOI: https://doi.org/10.1007/978-981-15-5577-0_61
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