A Customized Approach to Compress the Images with Deep Learning Model for Embedded System
Keywords:
Customized CNN, Compressing Images, Embedded systems, deep learning, image processing.Abstract
The automated systems like embedded systems will work autonomously, take decisions and actions independently. And they will use small resources like compressed memory unit and processing. But if we want intelligent system then we should integrate Machine Learning (ML) then the agent will take decisions and actions its own be fully autonomous. But Theses embedded systems used in scientific community required high computational speed Despite this when we are working with images, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. So this paper implements a Customized intelligent system to take image and compress the size of it, and send it to embedded system to take intelligent decisions. The proposed system is competitive if compared to other commercial systems with optimal results.
Downloads
References
Lee, J., Cho, S., & Beack, S. K. (2018). Context-adaptive entropy model for end-to-end optimized image compression. arXiv preprint arXiv:1809.10452.
Agustsson, E., Minnen, D., Toderici, G., & Mentzer, F. (2023). Multi-realism image compression with a conditional generator. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 22324-22333).
Ballé, J., Laparra, V., & Simoncelli, E. P. (2016). End-to-end optimized image compression. arXiv preprint arXiv:1611.01704.
Agustsson, E., Tschannen, M., Mentzer, F., Timofte, R., & Gool, L. V. (2019). Generative adversarial networks for extreme learned image compression. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 221-231).
Ballé, J., Minnen, D., Singh, S., Hwang, S. J., & Johnston, N. (2018). Variational image compression with a scale hyperprior. arXiv preprint arXiv:1802.01436.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
He, D., Zheng, Y., Sun, B., Wang, Y., & Qin, H. (2021). Checkerboard context model for efficient learned image compression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14771-14780).
Koyuncu, A. B., Gao, H., Boev, A., Gaikov, G., Alshina, E., & Steinbach, E. (2022, October). Contextformer: A transformer with spatio-channel attention for context modeling in learned image compression. In European Conference on Computer Vision (pp. 447-463). Cham: Springer Nature Switzerland.
Mentzer, F., Toderici, G. D., Tschannen, M., & Agustsson, E. (2020). High-fidelity generative image compression. Advances in Neural Information Processing Systems, 33, 11913-11924.
Yang, R., Van Gool, L., & Timofte, R. (2021). Perceptual learned video compression with recurrent conditional GAN. arXiv preprint arXiv:2109.03082, 1.
Zou, R., Song, C., & Zhang, Z. (2022). The devil is in the details: Window-based attention for image compression. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 17492-17501).
Rippel, O., & Bourdev, L. (2017, July). Real-time adaptive image compression. In International Conference on Machine Learning (pp. 2922-2930). PMLR.
Toderici, G., Vincent, D., Johnston, N., Jin Hwang, S., Minnen, D., Shor, J., & Covell, M. (2017). Full resolution image compression with recurrent neural networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (pp. 5306-5314).
Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing, 26(7), 3142-3155.
Lin, F., Sun, H., Liu, J., & Katto, J. (2023, June). Multistage spatial context models for learned image compression. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
Luo, W., Li, Y., Urtasun, R., & Zemel, R. (2016). Understanding the effective receptive field in deep convolutional neural networks. Advances in neural information processing systems, 29.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Fu, H., Liang, F., Lin, J., Li, B., Akbari, M., Liang, J., ... & Han, J. (2021). Learned image compression with discretized gaussian-laplacian-logistic mixture model and concatenated residual modules. arXiv preprint arXiv:2107.06463.
Santurkar, S., Budden, D., & Shavit, N. (2018, June). Generative compression. In 2018 Picture Coding Symposium (PCS) (pp. 258-262). IEEE.
Mahalingaiah, K., Sharma, H., Kaplish, P., & Cheng, I. (2020). Semantic learning for image compression (SLIC). In Smart Multimedia: Second International Conference, ICSM 2019, San Diego, CA, USA, December 16–18, 2019, Revised Selected Papers 2 (pp. 57-66). Springer International Publishing.
S. Hamis, T. Zaharia and O. Rousseau, "Image Compression at Very Low Bitrate Based on Deep Learned Super-Resolution," 2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT), Ancona, Italy, 2019, pp. 128-133, doi: 10.1109/ISCE.2019.8901038.
O. H. Nagoor, J. Whittle, J. Deng, B. Mora and M. W. Jones, "Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling," 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, pp. 2815-2819, doi: 10.1109/ICIP40778.2020.9191031.
Weber, M., Renggli, C., Grabner, H., Zhang, C. (2021). Observer Dependent Lossy Image Compression. In: Akata, Z., Geiger, A., Sattler, T. (eds) Pattern Recognition. DAGM GCPR 2020. Lecture Notes in Computer Science(), vol 12544. Springer, Cham
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
