A deep learning scheme for efficient multimedia IoT data compression

Wang, 2017, Multimedia iot systems and applications, 1 Al-Garadi, 2020, A survey of machine and deep learning methods for internet of things (iot) security, IEEE Commun. Surv. Tutor., 10.1109/COMST.2020.2988293 Aslam, 2021, A survey on object detection for the internet of multimedia things (iomt) using deep learning and event-based middleware: approaches, challenges, and future directions, Image Vis. Comput., 106, 10.1016/j.imavis.2020.104095 Nauman, 2020, Multimedia internet of things: A comprehensive survey, IEEE Access, 8, 8202, 10.1109/ACCESS.2020.2964280 Fizza, 2021, Qoe in iot: a vision, survey and future directions, Discov. Internet Things, 1, 1, 10.1007/s43926-021-00006-7 Wallace, 1992, The jpeg still picture compression standard, IEEE Trans. Consum. Electron., 38, xviii, 10.1109/30.125072 Bellard, 2014 Luo, 2021, A novel web attack detection system for internet of things via ensemble classification, IEEE Trans. Ind. Inf., 17, 5810, 10.1109/TII.2020.3038761 Sori, 2021, Dfd-net: lung cancer detection from denoised ct scan image using deep learning, Front. Comput. Sci., 15, 1, 10.1007/s11704-020-9050-z Qiu, 2019, Nei-tte: intelligent traffic time estimation based on fine-grained time derivation of road segments for smart city, IEEE Trans. Ind. Inf., 16, 2659, 10.1109/TII.2019.2943906 Xu, 2020, Deep learning based emotion analysis of microblog texts, Inf. Fusion, 64, 1, 10.1016/j.inffus.2020.06.002 Hu, 2021, Deep-green: A dispersed energy-efficiency computing paradigm for green industrial iot, IEEE Trans. Green Commun. Netw., 5, 750, 10.1109/TGCN.2021.3064683 Li, 2018, Learning iot in edge: Deep learning for the internet of things with edge computing, IEEE Network, 32, 96, 10.1109/MNET.2018.1700202 Tang, 2017, Enabling deep learning on iot devices, Computer, 50, 92, 10.1109/MC.2017.3641648 Tian, 2018, Deep learning for image denoising: a survey, 563 Tian, 2020, Deep learning on image denoising: An overview, Neural Netw., 10.1016/j.neunet.2020.07.025 Bai, 2020, Survey of learning based single image super-resolution reconstruction technology, Pattern Recognit. Image Anal., 30, 567, 10.1134/S1054661820040045 Wang, 2020, Deep learning for image super-resolution: A survey, IEEE Trans. Pattern Anal. Mach. Intell. Yan, 2020, Deep multi-view enhancement hashing for image retrieval, IEEE Trans. Pattern Anal. Mach. Intell., 43, 1445, 10.1109/TPAMI.2020.2975798 Yan, 2020, Depth image denoising using nuclear norm and learning graph model, ACM Trans. Multimed. Comput., Commun. Appl., 16, 1, 10.1145/3404374 Yan, 2021, Precise no-reference image quality evaluation based on distortion identification, ACM Trans. Multimed. Comput., Commun. Appl., 10.1145/3468872 Yan, 2021, Task-adaptive attention for image captioning, IEEE Trans. Circuits Syst. Video Technol. Yan, 2021, Age-invariant face recognition by multi-feature fusion and decomposition with self-attention, ACM Trans. Multimed. Comput. Commun. Appl. Skodras, 2001, The jpeg 2000 still image compression standard, IEEE Signal Process. Mag., 18, 36, 10.1109/79.952804 Albalawi, 2015, A hardware architecture for better portable graphics (bpg) compression encoder, 291 Sze, 2014, High efficiency video coding (hevc), vol. 39, 40 Wiegand, 2003, Overview of the h. 264/avc video coding standard, IEEE Trans. Circuits Syst. Video Technol., 13, 560, 10.1109/TCSVT.2003.815165 Dong, 2014, Learning a deep convolutional network for image super-resolution, 184 Dong, 2016, Accelerating the super-resolution convolutional neural network, 391 Jiwon Kim, Jung.Kwon Lee, Kyoung Mu Lee, Accurate image super-resolution using very deep convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 1646–1654. Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-recursive convolutional network for image super-resolution, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 1637–1645. Ying Tai, Jian Yang, Xiaoming Liu, Image super-resolution via deep recursive residual network, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3147–3155. Ying Tai, Jian Yang, Xiaoming Liu, Chunyan Xu, Memnet: A persistent memory network for image restoration, in: Proceedings of the IEEE international conference on computer vision, 2017, pp. 4539–4547. Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photo-realistic single image super-resolution using a generative adversarial network, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4681–4690. Tong Tong, Gen Li, Xiejie Liu, Qinquan Gao, Image super-resolution using dense skip connections, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 4799–4807. Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, Yun Fu, Residual dense network for image super-resolution, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 2472–2481. Muhammad Haris, Gregory Shakhnarovich, Norimichi Ukita, Deep back-projection networks for super-resolution, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 1664–1673. Wang, 2018, Deep bi-dense networks for image super-resolution, 1 Li, 2021, Rgsr: A two-step lossy jpg image super-resolution based on noise reduction, Neurocomputing, 419, 322, 10.1016/j.neucom.2020.08.056 Qiu, 2020, Deep residual learning-based enhanced jpeg compression in the internet of things, IEEE Trans. Ind. Inf., 17, 2124 Krishnaraj, 2020, Deep learning model for real-time image compression in internet of underwater things (iout), J. Real-Time Image Process., 17, 2097, 10.1007/s11554-019-00879-6 Anelli, 2020, Deep learning-based adaptive image compression system for a real-world scenario, 1 Azar, 2019, An energy efficient iot data compression approach for edge machine learning, Future Gener. Comput. Syst., 96, 168, 10.1016/j.future.2019.02.005 Mentzer, 2020, High-fidelity generative image compression, Adv. Neural Inf. Process. Syst., 33 2004 2020 Kumar, 2018, F-des: Fast and deep event summarization, IEEE Trans. Multimed., 20, 323, 10.1109/TMM.2017.2741423 Kumar, 2018, Eratosthenes sieve based key-frame extraction technique for event summarization in videos, Multimedia Tools Appl., 77, 7383, 10.1007/s11042-017-4642-9 Kumar, 2018, Deep event learning boost-up approach: Delta, Multimedia Tools Appl., 77, 26635, 10.1007/s11042-018-5882-z Kumar, 2018, Esumm: Event summarization on scale-free networks, IETE Tech. Rev. Kumar, 2019, Evs-dk: Event video skimming using deep keyframe, J. Vis. Commun. Image Represent., 58, 345, 10.1016/j.jvcir.2018.12.009 Kumar, 2021, Text query based summarized event searching interface system using deep learning over cloud, Multimedia Tools Appl., 80, 11079, 10.1007/s11042-020-10157-4