Article
Computer Science, Information Systems
Hadi Hadizadeh, Ivan V. Bajic
Summary: This paper presents a soft video multicasting system using adaptive block-based compressed sensing method, in which each block in each frame of the input video is adaptively sampled based on texture complexity and visual saliency. The system achieves better reconstruction quality at the decoder side using an iterative algorithm and adaptive processing, exploiting temporal similarity between adjacent frames. Extensive experimental results demonstrate the superiority of this system over existing soft video multicasting systems.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Optics
Wang Hao-quan, Tang Qian-nan, Ren Shi-lei
Summary: This paper investigates distributed video coding based on compressed sensing theory, using the discriminative K-SVD algorithm. Experimental results demonstrate that the proposed method achieves better reconstruction results and saves a significant amount of computing time.
Article
Computer Science, Artificial Intelligence
Peilin Chen, Wenhan Yang, Meng Wang, Long Sun, Kangkang Hu, Shiqi Wang
Summary: This paper proposes a novel approach for deep video super-resolution in the compressed domain, utilizing coding priors and deep priors to reconstruct high-resolution videos effectively. The incorporation of the GSFT layer and guided soft alignment scheme, combining spatial and temporal coding priors, leads to more effective video reconstruction.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Qing Ding, Liquan Shen, Liangwei Yu, Hao Yang, Mai Xu
Summary: The study introduces a patch-wise spatial-temporal quality enhancement network that outperforms traditional methods, achieving improved video quality in scene-changing and strong-motion videos.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Engineering, Electrical & Electronic
Ahmed Roushdy Elkordy, Songze Li, Mohammad Ali Maddah-Ali, A. Salman Avestimehr
Summary: Communication overhead is a major bottleneck in large-scale distributed computing systems, particularly for machine learning applications. The development of coded distributed computing has shown that coding opportunities across different computation tasks can reduce communication load. Compressed coded distributed computing combines compression and coding techniques to significantly reduce communication load, outperforming conventional methods and CDC schemes.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Wuzhen Shi, Shaohui Liu, Feng Jiang, Debin Zhao
Summary: This paper proposes a novel video CS framework called VCSNet based on convolutional neural networks, aiming to explore both intraframe and interframe correlations. This method shows better objective and subjective reconstruction quality compared to existing methods in experiments.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Shuai Zheng, Jian Chen, Xiao-Ping Zhang, Yonghong Kuo
Summary: This paper proposes a novel multihypothesis-based distributed compressed video sensing system that improves system accuracy and performance through new acquisition and weight prediction methods.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Engineering, Electrical & Electronic
Jianyi Wang, Mai Xu, Xin Deng, Liquan Shen, Yuhang Song
Summary: This paper focuses on enhancing the perceptual quality of compressed video and proposes a novel generative adversarial network based on multi-level wavelet packet transform to exploit high-frequency details for enhancing video quality. Experimental results demonstrate the superiority of the proposed method in enhancing the perceptual quality of compressed video.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Zhenyu Guan, Qunliang Xing, Mai Xu, Ren Yang, Tie Liu, Zulin Wang
Summary: This paper proposes a Multi-Frame Quality Enhancement (MFQE) approach for compressed video, utilizing a Multi-Frame Convolutional Neural Network to enhance the quality of low-quality frames and improve the overall quality of compressed video.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Engineering, Multidisciplinary
Li Li, Miaomiao Zhou, Ye Zhu, Ya Dai, Xuwen Liang
Summary: In this study, distributed compressed sensing (DCS) was applied to microvibration measurement on satellites to reduce the burden of multiple sensors with limited resources. An improved joint recovery algorithm was proposed, achieving a signal difference of less than 4% in power spectrum density (PSD) with one-fifth sampling points of the raw signal. The results demonstrate the feasibility of satellite microvibration measurement based on DCS.
Article
Computer Science, Information Systems
Hua Xiao, Zhongliang Wang, Xueying Cui
Summary: This paper proposes a distributed compressed sampling strategy to collect compressed hyperspectral data, achieving significant advantage in image reconstruction performance by exploring abundance and endmember estimation methods and applying a linear mixing model for hyperspectral images.
Article
Chemistry, Analytical
Tien-Ying Kuo, Yu-Jen Wei, Po-Chyi Su, Chang-Hao Chao
Summary: In order to address the issue of content loss and visual artifacts caused by video compression algorithms, we propose a learning-based restoration method that predicts the difference between original and compressed video frames. We utilize a recursive neural network model with dilated convolution, as well as a temporal fusion module and integrated color channels. Our lightweight model outperforms other methods, with an average improvement of 0.18 dB of BD-PSNR and -5.06% of BD-BR in compressed video quality on the HEVC test model (HM).
Review
Neurosciences
Biao Sun, Wenfeng Zhao
Summary: This article provides a comprehensive survey of literature on compressed sensing of neurophysiology signals, discussing its applications, technical challenges, and prospects in neural signal transmission.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Computer Science, Information Systems
Nan Cen, Zhangyu Guan, Tommaso Melodia
Summary: In this paper, a novel paradigm for compressed-sensing-enabled multi-view coding and streaming in WMSN is proposed. The architecture leverages the properties of Compressed Sensing (CS) to overcome the limitations of traditional encoding techniques and can transmit multi-view streams with guaranteed video quality at lower power consumption.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Engineering, Electrical & Electronic
Yongjeong Oh, Namyoon Lee, Yo-Seb Jeon, H. Vincent Poor
Summary: In this paper, a communication-efficient federated learning framework is presented, inspired by quantized compressed sensing. The framework includes gradient compression for wireless devices and gradient reconstruction for a parameter server. By leveraging both dimension reduction and quantization, a higher compression ratio than one-bit gradient compression can be achieved. An approximate minimum mean square error (MMSE) approach for gradient reconstruction using the expectation-maximization generalized-approximate-message-passing (EM-GAMP) algorithm is proposed for accurate aggregation of local gradients from the compressed signals.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Xueyu Han, Ishtiaq Rasool Khan, Susanto Rahardja
Summary: This paper proposes a clustering-based TMO method by embedding human visual system models to adapt to different HDR scenes. The method reduces computational complexity using a hierarchical scheme for clustering and enhances local contrast by superimposing details and controlling color saturation by limiting the adaptive saturation parameter. Experimental results show that the proposed method achieves improvements in generating high quality tone-mapped images compared to competing methods.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2024)
Article
Engineering, Electrical & Electronic
Zuopeng Zhao, Tianci Zheng, Kai Hao, Junjie Xu, Shuya Cui, Xiaofeng Liu, Guangming Zhao, Jie Zhou, Chen He
Summary: The research team developed a handheld phone detection network called YOLO-PAI, which successfully achieved real-time detection and underwent testing under various conditions. Experimental results show that YOLO-PAI reduces network structure parameters and computational costs while maintaining accuracy, outperforming other popular networks in terms of speed and accuracy.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2024)
Article
Engineering, Electrical & Electronic
Vivek Sharma, Ashish Kumar Tripathi, Purva Daga, M. Nidhi, Himanshu Mittal
Summary: In this study, a novel ClGan method is proposed for automated plant disease detection. The method reduces the number of parameters and addresses the issues of vanishing gradients, training instability, and non-convergence by using an encoder-decoder network. Additionally, an improved loss function is introduced to stabilize the learning process and optimize weights effectively. Furthermore, a new plant leaf classification method called ClGanNet is introduced, achieving 99.97% training accuracy and 99.04% testing accuracy using the least number of parameters.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2024)
Article
Engineering, Electrical & Electronic
Seongeun Kim, Chang-Ock Lee
Summary: This article introduces a method for segmenting individual teeth in human teeth images by using deep neural networks to obtain pseudo edge-regions and applying active contour models for segmentation.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2024)