Motion-Texture Joint Threshold-Adaptive Skipping for Distributed Video Compressive Sensing

Abstract

The traditional distributed video compressive sensing (DVCS) system effectively reduces transmission costs and encoder complexity for wireless visual sensor networks (WVSN), but struggles with dynamic scenes involving rapid motion or complex textures. Their reliance on temporal correlation and skipping causes reconstruction artifacts and loss of structural details, especially at high skip ratios. This paper proposes the motion-texture joint (MTJ) algorithm to enhance DVCS performance, where the motion module first preliminarily screens those candidate skip-blocks using the sum of absolute differences (SAD) and then generates more robust side information by combining frame interpolation with motion vectors for redundant-content identification and skip-block decision. Further, a texture feature module based on local binary pattern (LBP) analysis jointly exploits LBP texture features, variance textures, and gradient energy textures from adjacent frames, which improves inter-frame correlations. On this basis, an MTJ adaptive weight module dynamically allocates coefficients to motion information and texture information according to their spatio-temporal contributions and controls a skip ratio of DVCS. Experiments on standard video sequences demonstrate that the MTJ algorithm achieves an improvement in average peak signal-to-noise ratio (PSNR) of 0.12 dB over state-of-the-art uniform-reference threshold-dynamic skipping (UTS) with minimal encoder complexity increase. Especially at a 70% skip ratio, the maximum PSNR improvement over UTS reaches up to 0.20 dB on the Foreman sequence. MTJ algorithm effectively balances reconstruction quality and computational complexity, making it suitable for WVSN applications.

Keywords

distributed video compressive sensing (DVCS) / skip ratio / motion estimation / local binary pattern (LBP) texture feature / adaptive weight

References

[1] Spagnolo F, Lal B, Corsonello P, et al. A novel compressive sensing method for secure and energy efficient ECG signal transmission applications[J]. IEEE Journal of Biomedical and Health Informatics, 2025, 29(5): 3439-3450.

[2] Zeng C Y, Zou F, Xia S Y, et al. SAU—Net: saliency—based adaptive unfolding network for interpretable high—quality image compressed sensing in Internet of Things[J]. IEEE Internet of Things Journal, 2025, 12(14): 28672-28688.

[3] Unde A S, Pattathil D P. Adaptive compressive video coding for embedded camera sensors: compressed domain motion and measurements estimation[J]. IEEE Transactions on Mobile Computing, 2020, 19(10): 2250-2263.

[4] Guan Z Y, Xing Q L, Xu M, et al. MFQE 2.0: a new approach for multi—frame quality enhancement on compressed video[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(3): 949-963.

[5] Chen C, Wu Y T, Zhou C, et al. JsrNet: a joint sampling—reconstruction framework for distributed compressive video sensing[J]. Sensors, 2020, 20(1): 206.

[6] Song X D, Peng X L, Xu J Z, et al. Distributed compressive sensing for cloud—based wireless image transmission[J]. IEEE Transactions on Multimedia, 2017, 19(6): 1351-1364.

[7] Zhang Y F, Zhang C, Fan R, et al. Recent advances on HEVC inter—frame coding: from optimization to implementation and beyond[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(11): 4321-4339.

[8] Song Z, Chen J H. Adaptive rate compression for distributed video sensing in wireless visual sensor networks[J]. The Visual Computer, 2025, 41(10): 7909-7923.

[9] Zhao C, Ma S W, Zhang J, et al. Video compressive sensing reconstruction via reweighted residual sparsity[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(6): 1182-1195.

[10] Yuan W Y, Liu H. Motion—adaptive adjacent—reference skipping for distributed video compressive sensing with general decoders[J]. Optoelectronics Letters, 2022, 18(12): 755-762.

[11] Priya G L, Ghosh D. An effectual video compression scheme for WVSNs based on block compressive sensing[J]. IEEE Transactions on Network Science and Engineering, 2024, 11(2): 1542-1552.

[12] Liu H, Sun R H. Uniform—reference threshold—dynamic skipping for video compressive sensing[J]. Displays, 2025, 87: 102963.

[13] Yang X, Yang C L. MAP—inspired deep unfolding network for distributed compressive video sensing[J]. IEEE Signal Processing Letters, 2023, 30: 309-313.

[14] Wang Q R, Qian L P, Jiang W, et al. Integrated communication and computation resource allocation for the compressive sensing based image transmission[J]. IEEE Transactions on Mobile Computing, 2025, 24(10): 10510-10522.

[15] Zhang J G, Xiang Q M, Yin Y G, et al. Adaptive compressed sensing for wireless image sensor networks[J]. Multimedia Tools and Applications, 2017, 76(3): 4227-4242.

[16] Wang W, Li J J, Mo H, et al. Side information hybrid generation based on improved motion vector field[J]. Multimedia Tools and Applications, 2021, 80(17): 26713-26730.

[17] Li R, Yang Y H, Sun F Y. Green visual sensor of plant: an energy—efficient compressive video sensing in the Internet of Things[J]. Frontiers in Plant Science, 2022, 13: 849606.

[18] Song Z, Chen J H, Li K. A hybrid sampling mode design for compressive imaging based on adaptive block compressed sensing[C]// 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE). Piscataway, NJ: IEEE, 2025: 301-304.

[19] Park M, Bae Y. Complex motion—aware splatting for video frame interpolation[C]// 2023 14th International Conference on Information and Communication Technology Convergence (ICTC). Piscataway, NJ: IEEE, 2023: 1872-1876.

[20] Rup S, Majhi B, Padhy S. An improved side information generation for distributed video coding[J]. AEU—International Journal of Electronics and Communications, 2014, 68(3): 201-209.

[21] Ji Y K, Ge H Y, Meng Y Q, et al. MLGIA: recognition of traffic panel information based on PaddlePaddle[J]. Journal of Donghua University (English Edition), 2025, 42(5): 494-502.

[22] Zhou J, Liu H. Video compressive sensing reconstruction via long—short—term double—pattern prediction[J]. Optoelectronics Letters, 2020, 16(3): 230-236.

[23] Chen C, Zhou C, Liu P Y, et al. Iterative reweighted Tikhonov—regularized multihypothesis prediction scheme for distributed compressive video sensing[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(1): 1-10.

[24] Zeng X Y, Xu W J, Zhang X N. DrownACB—YOLO: an improved YOLO for drowning detection in swimming pools[J]. Journal of Donghua University (English Edition), 2025, 42(4): 417-424.