Enhanced Surveillance Video Compression with Dual Reference Frames Generation
Abstract
In this paper, we improve the inter coding performance of surveillance videos by simultaneously investigating the distinct characteristics of background and foreground redundancy, and introduce two novel reference frames in a complementary manner. On one hand, a block level background reference frame (BRF) is proposed to reduce the background redundancy. The proposed scheme incorporates semantic information into the compression process, and makes use of instance segmentation to facilitate the background block decision, making the generated BRF free from foreground pollution. On the other hand, in order to handle foreground redundancy, a foreground reference frame (FRF) is generated based on Surveillance Prediction Generative Adversarial Network (SP-GAN), which utilizes previous reconstructed frames, optical flow based prediction, as well as BRF to infer the foreground objects of the to-be-coded frame. We integrate the proposed scheme into HM-16.6 software and append BRF and FRF to the reference pictures list (RPS). Simulation results demonstrate considerable superiority of the proposed scheme. In particular, by adding the proposed BRF to RPS, 3% coding gains are observed compared with the state-of-the-art BRF method. When both BRF and FRF are incorporated into RPS, 5.8% gains are achieved for surveillance video coding.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Video compression involves reducing the size of video data to make it easier to store and transmit without sacrificing too much quality, which optimizes the use of bandwidth and reduces the cost of data storage and the transmission time.
“Video compression involves reducing the size of video data to make it easier to store and transmit without sacrificing too much quality, which optimizes the use of bandwidth and reduces the cost of data storage and the transmission time [29,33].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [33]
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137 · see citing works
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
Overview of the High Efficiency Video Coding (HEVC) Standard
Sullivan, G. J., Ohm, J.-R., Han, W.-J., & Wiegand, T. (2012) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2012.2221191 · Cited by 8,286 · see citing works
Adaptive background mixture models for real-time tracking
Stauffer, C., & Grimson, W. E. L. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1999.784637 · Cited by 6,925 · see citing works
“Least squares generative adversarial networks,”
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.304 · Cited by 5,437 · see citing works
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313 · see citing works
Unsupervised Learning of Video Representations using LSTMs
Srivastava, N., Mansimov, E., & Salakhutdinov, R. (2015) · arXiv · doi:10.48550/arxiv.1502.04681 · Cited by 1,861 · see citing works
Future Frame Prediction for Anomaly Detection - A New Baseline
Liu, W., Luo, W., Lian, D., & Gao, S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00684 · Cited by 1,601 · see citing works
Calcuation of average PSNR differences between RD-curves
Bjøntegaard, G. (2001) · Medical Entomology and Zoology · Cited by 1,408 · see citing works
Deep multi-scale video prediction beyond mean square errorPreprint
Mathieu, M., Couprie, C., & LeCun, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.05440 · Cited by 1,182 · see citing works
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
Hui, T.-W., Tang, X., & Loy, C. C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00936 · Cited by 817 · see citing works
Deep Predictive Coding Networks for Video Prediction and Unsupervised LearningPreprint
Lotter, W. E., Kreiman, G., & Cox, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.08104 · Cited by 805 · see citing works
Video Frame Synthesis Using Deep Voxel Flow
Liu, Z., Yeh, R. A., Tang, X., Liu, Y., & Agarwala, A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.478 · Cited by 766 · see citing works
Stable multi-target tracking in real-time surveillance video
Benfold, B., & Reid, I. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995667 · Cited by 651 · see citing works
Dual Motion GAN for Future-Flow Embedded Video Prediction
Liang, X., Lee, L., Dai, W., & Xing, E. P. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.194 · Cited by 342 · see citing works
A Novel Deep Learning-Based Method of Improving Coding Efficiency from the Decoder-End for HEVC
Wang, T., Chen, M., & Chao, H. (2017) · DCC · doi:10.1109/dcc.2017.42 · Cited by 187 · see citing works
Convolutional Neural Network-Based Block Up-Sampling for Intra Frame Coding
Li, Y., Liu, D., Li, H., Li, L., Wu, F., Zhang, H., & Yang, H. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2727682 · Cited by 157 · see citing works
Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding
Zhang, X., Huang, T., Tian, Y., & Gao, W. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2294549 · Cited by 106 · see citing works
Flexible Spatio-Temporal Networks for Video Prediction
Lu, C., Hirsch, M., & Schölkopf, B. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.230 · Cited by 91 · see citing works
Convolutional Neural Network-Based Fractional-Pixel Motion Compensation
Yan, N., Liu, D., Li, H., Li, B., Li, L., & Wu, F. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2816932 · Cited by 87 · see citing works
Enhanced Motion-Compensated Video Coding With Deep Virtual Reference Frame Generation
Zhao, L., Wang, S., Zhang, X., Wang, S., Ma, S., & Gao, W. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2913545 · Cited by 87 · see citing works
Spatial-temporal residue network based in-loop filter for video coding
Jia, C., Wang, S., Zhang, X., Wang, S., & Ma, S. (2017) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip.2017.8305149 · Cited by 76 · see citing works
One-for-All: Grouped Variation Network-Based Fractional Interpolation in Video Coding
Liu, J., Xia, S., Yang, W., Li, M., & Liu, D. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2882923 · Cited by 73 · see citing works
Zhang, X., Tian, Y., Huang, T., Dong, S., & Gao, W. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2352036 · Cited by 65 · see citing works
Explore and Model Better I-Frames for Video Coding
Paul, M., Lin, W., Lau, C.-T., & Lee, B. (2011) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2011.2138750 · Cited by 61 · see citing works
Transformation-Based Models of Video SequencesPreprint
van Amersfoort, J., Kannan, A., Ranzato, M., Szlam, A. D., Tran, D., & Chintala, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.08435 · Cited by 61 · see citing works
High Efficiency Video Coding(HEVC)
Rao, K. R., Kim, D. N., & Hwang, J. J. (2013) · Signals and communication technology · doi:10.1007/978-94-007-6742-3_5 · Cited by 60 · see citing works
An object-based video coding framework for video sequences obtained from static cameras
Hakeem, A., Shafique, K., & Shah, M. A. (2005) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1101149.1101289 · Cited by 56 · see citing works
Knowledge-Based Coding of Objects for Multisource Surveillance Video Data
Xiao, J., Hu, R., Liao, L., Chen, Y., Wang, Z., & Xiong, Z. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2581590 · Cited by 50 · see citing works
Block-Composed Background Reference for High Efficiency Video Coding
Chen, F., Li, H., Li, L., Liu, D., & Wu, F. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2593599 · Cited by 46 · see citing works
Video coding using the most common frame in scene
Paul, M., Lin, W., Lau, C. T., & Lee, B. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5495033 · Cited by 38 · see citing works
Low-complexity and high-efficiency background modeling for surveillance video coding
Zhang, X., Tian, Y., Huang, T., & Gao, W. (2012) · Visual Communications and Image Processing · doi:10.1109/vcip.2012.6410796 · Cited by 35 · see citing works
DYAN: A Dynamical Atoms-Based Network for Video Prediction
Liu, W., Sharma, A., Camps, O., & Sznaier, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_11 · Cited by 33 · see citing works
CNN-Based Bi-Directional Motion Compensation for High Efficiency Video Coding
Zhao, Z., Wang, S., Wang, S., Zhang, X., Ma, S., & Yang, J. (2018) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2018.8351189 · Cited by 31 · see citing works
Rate-Performance-Loss Optimization for Inter-Frame Deep Feature Coding From Videos
Ding, L., Tian, Y., Fan, H., Wang, Y., & Huang, T. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2745203 · Cited by 29 · see citing works
Overview of IEEE 1857 video coding standard
Ma, S., Wang, S., & Gao, W. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738308 · Cited by 27 · see citing works
Virtual Background Reference Frame Based Satellite Video Coding
Wang, X., Hu, R., Wang, Z., & Xiao, J. (2018) · IEEE Signal Processing Letters · doi:10.1109/lsp.2018.2862145 · Cited by 25 · see citing works
A novel video coding scheme using a scene adaptive non-parametric background model
Chakraborty, S., Paul, M., Murshed, M., & Ali, M. (2014) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2014.6958823 · Cited by 13 · see citing works
Surveillance video coding with vehicle library
Ma, C., Liu, D., Peng, X., & Wu, F. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296285 · Cited by 12 · see citing works
Hybrid Learning of Optical Flow and Next Frame Prediction to Boost Optical Flow in the WildPreprint
Sedaghat, N., Zolfaghari, M., & Brox, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.03777 · Cited by 10 · see citing works
Reference Clip for Inter Prediction in Video Coding
Ma, C., Liu, D., Peng, X., Wu, F., Li, H., & Wang, T. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2769884 · Cited by 10 · see citing works
Computing inverse optical flow
Sánchez, J., Salgado, A., & Monzón, N. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.09.009 · Cited by 9 · see citing works
VRFCNN: Virtual Reference Frame Generation Network for Quality SHVC
Ding, Q., Shen, L., Yang, H., Dong, X., & Xu, M. (2020) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.3037683 · Cited by 4 · see citing works
Flagged first, then by citations
1 of the 47 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
“Video compression involves reducing the size of video data to make it easier to store and transmit without sacrificing too much quality, which optimizes the use of bandwidth and reduces the cost of data storage and the transmission time [29,33].”
BackgroundOverview of intelligent video coding: from model-based to learning-based approaches
Ma, S., Gao, J., Wang, R., Chang, J., Mao, Q., Huang, Z., & Jia, C. (2023) · Visual Intelligence · doi:10.1007/s44267-023-00018-7 · Cited by 41 · see citing works
“[167] adopted previously reconstructed frames, optical flow-based prediction, and a background reference frame to infer the foreground objects of the frame to be coded.”
Deep Learning for Abnormal Human Behavior Detection in Surveillance Videos—A Survey
Wastupranata, L. M., Kong, S. G., & Wang, L. (2024) · Electronics · doi:10.3390/electronics13132579 · Cited by 49 · see citing works
End-to-End Neural Video Coding Using a Compound Spatiotemporal Representation
Liu, H., Lu, M., Chen, Z., Cao, X., Ma, Z., & Wang, Y. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3150014 · Cited by 38 · see citing works
End-To-End Compression for Surveillance Video With Unsupervised Foreground-Background Separation
Zhao, Y. Q., Luo, D., Wang, F., Gao, H., Ye, M., & Zhu, C. (2023) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2023.3280039 · Cited by 21 · see citing works
“Remark: Instead of the previous approaches that do a linear combination of the previous key-frame background and the current background [36] or update background based on motion estimation [26], [37], our method is simple and can be online.”
MethodFully Cascade Consistency Learning for One-Stage Object Detection
Wang, H., Jia, T., Ma, B., Wang, Q., & Zuo, W. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3263557 · Cited by 17 · see citing works
Innovative Insights: A Review of Deep Learning Methods for Enhanced Video Compression
Khadir, M., Hashmi, M. F., Kotambkar, D. M., & Gupta, A. D. (2024) · IEEE Access · doi:10.1109/access.2024.3450814 · Cited by 15 · see citing works
“Zhao et al. [84] enhanced the inter-coding performance of surveillance videos by examining definite foreground features as well as background repetition.”
BackgroundDepth Video Inter Coding Based on Deep Frame Generation
Li, G., Lei, J., Pan, Z., Peng, B., & Ling, N. (2024) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2024.3374103 · Cited by 9 · see citing works
“As an essential component of the video coding framework, inter prediction focuses on reducing temporal redundancy [6], [7], [8].”
BackgroundTemporal Adaptive Learned Surveillance Video Compression
Zhao, Y. Q., Ye, M., Ji, L., Guo, H., & Zhu, C. (2024) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2024.3434736 · Cited by 9 · see citing works
“For neural coding, all compression modules are implemented through neural networks [12], [13], [55], [56], [57].”
Dai, K., Zhu, J., Hou, H., Li, Q., Ma, X., & Yu, H. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3558243 · Cited by 8 · see citing works
“Assumption 1: There is no frame loss in either the image frame [36] C m or the point cloud frame P n before or after synchronization.”
Adaptive Surveillance Video Compression With Background Hyperprior
Zhao, Y. Q., Tang, S. Y., & Ye, M. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3521663 · Cited by 7 · see citing works
SWGNet: Step-Wise Reference Frame Generation Network for Multiview Video Coding
Zhang, J., Hou, Y., Pan, Z., Peng, B., Ling, N., & Lei, J. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3312213 · Cited by 7 · see citing works
“Zhao et al. [34] proposed a dual reference frame generation method, in which a background reference frame and a foreground reference frame are generated to reduce the background redundancy and foreground redundancy respectively.”
MethodSurveillance Video Representations for Bandwidth-limited Semantic Communications
Karaa, M., Bahria, R., Ghazzai, H., Setti, G., & Sboui, L. (2024) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshp64532.2024.11101151 · Cited by 6 · see citing works
“Zhao et al. improved surveillance video compression by combining instance segmentation and adversarial learning to generate better back-2024 ground reference frames without intersection with semantically important foreground objects [13].”
Foundation Model Empowered Real-Time Video Conference With Semantic Communications
Chen, M., Ma, W., Zeng, M., He, X., Xiong, J., Wang, L., Al‐Dulaimi, A., & Mumtaz, S. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3659719 · Cited by 5 · see citing works
“It introduces a huge semantic redundancy [13], [14].”
Model-based portrait video compression with spatial constraint and adaptive pose processing
Chen, X., Lei, W., Zhang, W., Meng, H., & Guo, H. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01499-2 · Cited by 5 · see citing works
“[24] designed Surveillance Prediction Generative Adversarial Network (SP-GAN) for the task of future frame prediction of surveillance videos.”
A deep learning behavior analysis model for efficient video surveillance using multi pose features
Shana, L., & Christopher, C. S. (2024) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2024.103245 · Cited by 4 · see citing works
A Novel Approach for Surveillance Compression using Neural Network Technique
Mohod, N., Agrawal, P., & Madaan, V. (2024) · International Research Journal of Multidisciplinary Technovation · doi:10.54392/irjmt2436 · Cited by 4 · see citing works
Weakly Supervised Video Re-Localization Through Multi-Agent-Reinforced Switchable Network
Zhou, Y., Guo, A., Huo, S., Liu, Y., & Kung, S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3347970 · Cited by 3 · see citing works
“Thus, it has considerable application potential in various fields, including video surveillance [19], video recommendation [20], and Pedestrian Trajectory Prediction [21].”
BackgroundA GAN–U-Net Hybrid Framework for Satellite Video Frame Interpolation
Daultani, G., BANSAL, N., Sahil, M., Mittal, A., & Tripathi, M. K. (2025) · International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN) · doi:10.1109/icecmsn68058.2025.11382703 · Cited by 2 · see citing works
“More recent work such as Super-SloMo [5], DAIN [6], and AdaCoF[7] tries to tackle occlusions and motion discontinuities more effectively.”
Research and Implementation of Constrained Keyframe-Based Virtual Human Video Generation
Li, P., Li, X., & Yin, L. (2024) · IEEE Access · doi:10.1109/access.2024.3465469 · Cited by 2 · see citing works
Padmapriya, D., & Ameelia Roseline, A. (2024) · Computational Intelligence · doi:10.1111/coin.70014 · Cited by 2 · see citing works
“In some scenarios, the bandwidth is constrained, and the transmission buffer is limited, leading to fluctuations in the resulting bit rate of the video encoder [11].”
Overview of Surveillance Video Compression for Power System
Li, L., Hu, X., Zhou, W., Zhang, P., & Cai, L. (2024) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice63571.2024.10863996 · Cited by 2 · see citing works
“By using background modeling methods to predict the background of input images, the redundancy of background encoding in surveillance videos can be reduced, effectively reducing the bit rate consumption of background encoding [9].”
Lokesh, K., & Baskar, M. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.9935 · Cited by 1 · see citing works
Semantic Maintained Video Compression by Background Blurring in Surveillance Scenarios
Cui, W., Zheng, X., Zhang, H., & Zeng, W. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3398-5_38 · Cited by 1 · see citing works
Pre-Trained Prior-Assisted Dynamic Point Cloud Compression via Feature Prediction
Zhou, Y., Zhang, X., Xu, Y., Zhang, K., & Zhang, L. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3686193 · Cited by 1 · see citing works
“Learning-based Static Point Cloud Compression: Inspired by deep learning’s success in image and video compression [20], [21], [22], [23], [24], [25], [26], numerous learning-based point cloud compression methods have emerged, outperforming traditional ones.”
HANeRV: Hierarchically Adaptive Neural Representation for Video Compression
Tang, L., ZHU, J., Zhang, X., Zhang, L., Ma, S., & Huang, Q. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3696103 · Cited by 1 · see citing works
“Furthermore, we also select 7 surveillance video sequences from works [99], [100], and 7 screen content coding (SCC) video sequences [101] to further assess the performance of our HANeRV.”
StableFlow: Real-Time 4K Video Super-Resolution With Robust Feature Propagation
Chen, K., Zhang, X., Huang, A., Gao, W., & Zhao, T. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3728596 · Cited by 1 · see citing works
“ultra-high-definition standards, the demand for high-quality 4K video content has increased significantly across various applications, including entertainment, surveillance, medical imaging, and remote monitoring [1], [2], [3].”
Sameerunnisa, S., & Jabez, J. (2023) · Revue d intelligence artificielle · doi:10.18280/ria.370215 · Cited by 1 · see citing works
FCEGNet: Feature calibration and edge-guided MLP decoder Network for RGB-D semantic segmentation
Lu, Y., Ge, B., Xia, C., Zhu, X., Zhang, M., Gao, M., Chen, N., Hu, J., & Zhi, J. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104448 · Cited by 2 · see citing works
Pal, A. K., Biswas, B., Jichkar, M. D., Jena, A. N., & Kumar, M. (2023) · Research Square · doi:10.21203/rs.3.rs-3638041/v1 · Cited by 1 · see citing works
A Survey on Hiding Data Using Video Steganography
Sk, S., & Aruna, O. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-1726-6_31
Wastupranata, L. M. (2024) · doi:10.36227/techrxiv.170473723.30823862/v1
Region Motion-based Adaptive Composite Long-Term Reference Coding for VVC
Zheng, X., Liu, Y., Wang, J., Ren, Z., Zhu, S., & Liao, Q. (2024) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas58744.2024.10558298
“The third one is deep-learning-based method which synthesizes LTR picture with deep neural network [7]–[9].”
MethodFPSNet: Focus-Perceptual-Semantic Full Flow Visual Redundancy Predicting for Camera Image
Xiao, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8692-3_2
Object detection driven composite block motion estimation algorithm for surveillance video coding
Pal, A. K., Biswas, B., Jichkar, M. D., Jena, A. N., & Kumar, M. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20722-4
“Zhao et al. [31] proposed a method to enhance surveillance video compression performance by addressing background and foreground redundancy.”
A Comprehensive Review on the Prediction of Video Frame and Motion Contents
Mohana Priya., P., & Ulagapriya, K. (2024) · International Conference on Integration of Emerging Technologies for the Digital World (ICIETDW) · doi:10.1109/icietdw61607.2024.10939518
“L.Zhao et al. (2021) improved the performance of the future frame prediction of the video signal by using the Background Reference Frame (BRF) [10].”
EcoVis: Towards Energy and Connectivity Optimized Visual Surveillance
Lenka, M. K., & Chakraborty, A. (2025) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom64205.2025.00023
“Methods such as motion estimation [16], [17], background subtraction [18], [19], and deep learning-based techniques [20], [21], [22] including attention based mechanisms [23], [24] are widely used for R O I identification.”
Personalized Semantic Feature Reconstruction Based Generative Portrait Video Compression
Chen, X., Lei, W., Zhang, D., & Zhang, Y. (2025) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit65432.2025.11035698
“A single reference frame is adopted in [6] and [15], whereas Zhao et al. [16] using multiple reference frames.”
Lokesh, K., & Baskar, M. (2025) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon65728.2025.11071084
“A method utilizing a block-level background reference frame (BRF) is introduced in [4] to address redundancy challenges.”
Efficient Video Surveillance-Based Malicious Activity Monitoring Using Deep Learning: A Survey
Lokesh, K., & Baskar, M. (2025) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-031-84513-0_26
Lokesh, K., & Baskar, M. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.11370
“Background reference features further enhance detection accuracy: a block-level Background Reference Frame (BRF) method [4] reduces redundancy by constructing reference frames from Surveillance Prediction Generative Adversarial Network (SPGAN) outputs and previous frames, with predictions informed…”
Rizk, M., Taleb, H., Rhayem, A., Chaaya, J. A., Zaki, C., & Nasser, A. (2026) · Research Square · doi:10.21203/rs.3.rs-8815873/v1
Enhancing Surveillance Storage Through Selective Frame Retention and Deep Semantic Analysis
Lin, P.-J., Tsai, M.-S., Chen, Y., & Yang, T. (2026) · IET Image Processing · doi:10.1049/ipr2.70473
- Authors
- Zhao, L., Wang, S., Wang, S., Ye, Y., Ma, S., & Gao, W.
- Affiliations
- Peking University (CN)
- City University of Hong Kong (HK)
- Alibaba Group (United States) (US)
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2022-03
- Volume
- 32
- Issue
- 3
- Pages
- 1592-1606
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W3152846589
- Cited by
- 43
- References
- 47
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 16 · 2023: 9 · 2022: 1
- FWCI
- 3.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Circuits and Systems for Video Technology (publishedVersion)
- Funders
- Key-Area Research and Development Program of Guangdong Province (2019B010133001)
- PKU-Alibaba Fund
- High-Performance Computing Platform of Peking University
- Topics
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- inter coding, instance segmentation, generative adversarial networks, optical flow prediction, reference picture selection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
