东华大学学报（英文版）2026，Vol.43Issue(4)：67-82,16.DOI:10.19884/j.1672-5220.202512016
面向分布式视频压缩感知的运动-纹理联合阈值-自适应跳块算法
Motion-Texture Joint Threshold-Adaptive Skipping for Distributed Video Compressive Sensing
摘要
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.关键词
分布式视频压缩感知/跳块率/运动估计/局部二值模式纹理特征/自适应权重Key words
distributed video compressive sensing(DVCS)/skip ratio/motion estimation/local binary pattern(LBP)texture feature/adaptive weight分类
信息技术与安全科学引用本文复制引用
苏金昊,刘浩,黄荣..面向分布式视频压缩感知的运动-纹理联合阈值-自适应跳块算法[J].东华大学学报（英文版）,2026,43(4):67-82,16.基金项目
National Natural Science Foundation of China(Nos.62372100 and 62571110) （Nos.62372100 and 62571110）
