Abstract
In this paper, a method is proposed to improve the image quality of clients based on the information of the residual images stored in the edge. A large amount of image information is stored in the cloud, whose image quality can meet the requirements of users in most cases, but for individual high-value targets, a higher image quality is needed. With the compressed information of the residual images stored at the edge, the quality of client images can be effectively improved. In this paper, firstly, an original image is compressed by JPEG with different quality factors and subtracted to obtain the residual images, which are then compressed by a proposed generative adversarial network, and the 1/100 one-dimensional data is stored in the edge. Secondly, when users receive a basic image, the quality of the final image is improved by decompressing the residual data. The compressed data can be appropriately removed and the image quality can be improved according to different needs. The experimental results show that, compared with the original compression method, through the cloud edge client collaboration method, not only can the image quality be improved, but the amount of data stored and transmitted in the cloud can also be effectively reduced, fully reflecting the advantages of on-demand service.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sunyaev, A.: Cloud computing. In: Internet Computing, pp. 195–236. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-34957-8_7
Raid, A.M., Khedr, W.M., El-Dosuky, M.A., et al.: JPEG image compression using discrete cosine transform-a survey. arXiv preprint arXiv:1405.6147 (2014)
Zhou, Z., Wang, Y., Guo, Y., et al.: Image quality improvement of hand-held ultrasound devices with a two-stage generative adversarial network. IEEE Trans. Biomed. Eng. 67(1), 298–311 (2019)
Zhai, G., Min, X.: Perceptual image quality assessment: a survey. Sci. China Inf. Sci. 63, 1–52 (2020)
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)
Rippel, O., Bourdev, L.: Real-time adaptive image compression. In: International Conference on Machine Learning, pp. 2922–2930. PMLR, July 2017
Santurkar, S., Budden, D., Shavit, N.: Generative compression. In: 2018 Picture Coding Symposium (PCS), pp. 258–262. IEEE, June 2018
Liu, Q.: Image compression based on generative adversarial networks. Xidian University (2019)
Liu, J., Lu, G., Hu, Z., Xu, D.: A unified end-to-end framework for efficient deep image compression. arXiv preprint arXiv:2002.03370 (2020)
Acknowledgement
This work was supported by National Key Laboratory Foundation (No. 6142411204306).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, M., Li, X., Shao, Y., Zhang, J., Ding, Y., Zhou, Q. (2022). An Image Quality Improvement Method Through Generative Adversarial Networks in Cloud Edge Integration. In: Li, X. (eds) Advances in Intelligent Automation and Soft Computing. IASC 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-81007-8_80
Download citation
DOI: https://doi.org/10.1007/978-3-030-81007-8_80
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-81006-1
Online ISBN: 978-3-030-81007-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)