Advanced Imaging, Volume. 1, Issue 2, 021002(2024)
Block-modulating video compression: an ultralow complexity image compression encoder for resource-limited platforms
[5] E. K. Ryu et al. Plug and-play methods provably converge with properly trained denoisers, 5546(2019).
[6] S. V. Venkatakrishnan, C. A. Bouman, B. Wohlberg. Plug-and-play priors for model based reconstruction, 945(2013).
[8] X. Yuan et al. Plug-and-play algorithms for large-scale snapshot compressive imaging, 1447(2020).
[13] X. Yuan et al. Low-cost compressive sensing for color video and depth, 3318(2014).
[16] Z. Meng, J. Ma, X. Yuan. End-to-end low cost compressive spectral imaging with spatial-spectral self-attention, 187(2020).
[22] Y. Sun, X. Yuan, S. Pang. Compressive high-speed stereo imaging. Opt. Express, 25, 18182(2017).
[24] A. Buades, B. Coll, J.-M. Morel. A non-local algorithm for image denoising, 60(2005).
[28] X. Yuan. Generalized alternating projection based total variation minimization for compressive sensing, 2539(2016).
[30] X. Miao et al. λ-net: Reconstruct hyperspectral images from a snapshot measurement, 4059(2019).
[31] Z. Cheng et al. Memory-efficient network for large-scale video compressive sensing, 16246(2021).
[34] J. Ma et al. Deep tensor ADMM-Net for snapshot compressive imaging, 10223(2019).
[35] L. Wang et al. Hyperspectral image reconstruction using a deep spatial-spectral prior, 8024(2019).
[36] Y. Li et al. End-to-end video compressive sensing using Anderson-accelerated unrolled networks, 1(2020).
[37] T. Huang et al. Deep Gaussian scale mixture prior for spectral compressive imaging, 16216(2021).
[38] K. Greger, Y. LeCun. Learning fast approximations of sparse coding, 399(2010).
[39] Y. Yang et al. Deep ADMM-Net for compressive sensing MRI. Advances in Neural Information Processing Systems, 29, 10(2016).
[40] C. A. Metzler, A. Mousavi, R. G. Baraniuk. Learned D-AMP: principled neural network based compressive image recovery, 1770(2017).
[47] J. R. Hershey, J. L. Roux, F. Weninger. Deep unfolding: model-based inspiration of novel deep architectures(2014).
[48] O. Ronneberger, P. Fischer, T. Brox. U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 9351, 234(2015).
[49] S. Nah, T. Hyun Kim, K. Mu Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring, 3883(2017).
[50] F. Perazzi et al. A benchmark dataset and evaluation methodology for video object segmentation, 724(2016).
[52] A. Mercat, M. Viitanen, J. Vanne. UVG dataset: 50/120 fps 4k sequences for video codec analysis and development, 297(2020).
[55] J. Zhang, B. Ghanem. ISTA-Net: interpretable optimization-inspired deep network for image compressive sensing, 1828(2018).
[57] Lu Gan. Block compressed sensing of natural images, 403(2007).
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Siming Zheng, Yujia Xue, Waleed Tahir, Zhengjue Wang, Hao Zhang, Ziyi Meng, Gang Qu, Siwei Ma, Xin Yuan, "Block-modulating video compression: an ultralow complexity image compression encoder for resource-limited platforms," Adv. Imaging 1, 021002 (2024)
Category: Research Article
Received: Jan. 22, 2024
Accepted: Jul. 9, 2024
Published Online: Aug. 8, 2024
The Author Email: Xin Yuan (xyuan@westlake.edu.cn)