计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 190-196.doi: 10.11896/jsjkx.200800197

• 计算机图形学与多媒体 • 上一篇    下一篇

面向分块压缩感知的交叉子集导引自适应观测

田伟1, 刘浩1,2, 陈根龙1, 宫晓蕙1   

  1. 1 东华大学信息科学与技术学院 上海 201620
    2 人工智能教育部重点实验室 上海 200240
  • 收稿日期:2020-08-28 修回日期:2020-09-25 发布日期:2020-12-17
  • 通讯作者: 刘浩(liuhao@dhu.edu.cn)
  • 作者简介:2903502388@qq.com
  • 基金资助:
    上海市自然科学基金项目(18ZR1400300);人工智能教育部重点实验室开放基金

Cross Subset-guided Adaptive Measurement for Block Compressive Sensing

TIAN Wei1, LIU Hao1,2, CHEN Gen-long1, GONG Xiao-hui1   

  1. 1 College of Information Science and Technology Donghua University Shanghai 201620,China
    2 Key Laboratory of Artificial Intelligence Ministry of Education Shanghai 200240,China
  • Received:2020-08-28 Revised:2020-09-25 Published:2020-12-17
  • About author:TIAN Wei,born in 1995postgraduateis a member of China Computer Federation.His main research interests include image compression sensing and so on.
    LIU Hao,born in 1977associate professoris a member of China Computer Federation.His main research interests include multimedia signal processing and intelligent sensing system.
  • Supported by:
    Natural Science Foundation of Shanghai (18ZR1400300) and Foundation of Key Laboratory of Artificial Intelligence,Ministry of Education,P.R. China.

摘要: 相比传统的图像信号处理方法分块压缩感知能够以较低的复杂度实现图像信号的采集与编码这为功耗受限的无线传感设备提供了一种较为理想的选择方案.针对传感图像的分块压缩感知提出了一种结合螺旋顺序的交叉子集导引自适应观测方法通过为不同区域分配与其内容大小相适应的采样率并且融入观测块预测可以在提高图像重构质量的同时提升观测块的编码效率.所提方法以一幅图像的中心块为起点采用螺旋式扫描顺序将图像平均分成内区、中区、外区3个区域将每个区域每隔若干块放入交叉子集3个区域的交叉子集块以基本采样率进行采样观测根据得到的观测数据结果按权重自适应分配不同的采样率给3个区域的剩余子集剩余子集分别采用给定的自适应采样率进行采样观测.此外对于每一个剩余子集中的观测块所提方法自适应地扩大该观测块的周围邻域来寻找最佳预测块对预测差值进行标量量化.实验结果表明与目前比较流行的观测方法相比所提方法不仅可以在主观上改善图像重构质量还可以在客观上将图像重构的平均率失真性能至少提升3.2%.

关键词: 分块压缩感知, 交叉子集, 块预测, 率失真, 剩余子集, 自适应采样率

Abstract: Compared with traditional image processing methodsthe block compressive sensing can concurrently finish both acquisition and compression with a very low complexitywhich will be an ideal choice for some wireless sensors with limited power.For block compressive sensing of any imagethis paper proposes a cross subset-guided adaptive measurement method.The proposed method can adaptively allocate its sampling subrate to different regionsand also introduce the spatial prediction of mea-surement blockswhich effectively improves the quality of image reconstruction and the coding efficiency of measurement blocks.Specificallystarting from the center block in the spiral scanning orderall blocks of any image are divided into three regions:inner regionmiddle regionand outer region.Every few blocks of each region are put into a cross subset.Firstlythese blocks of each cross subset are measured by the same measurement matrix at a basic sampling subrate.Secondlyaccording to the cross-subset measurement values of three regionstheir weights are used to assign different sampling subrates for the remaining subset.Thirdlythe remaining-subset blocks of the three regions are measured by different sampling subrateswhich are proportional to their weights.For each measurement blockthe optimal predictive block is found from the surrounding area of the measurement blockand the difference between them is quantized by scalar quantization.The experimental results show that compared with theexis-ting measurement methodsthe proposed method not only improves the subjective quality of reconstructed imagebut also improves the average rate-distortion performance of image reconstruction by at least 3.2%.

Key words: Adaptive sampling subrate, Block compressive sensing, Block prediction, Cross subset, Rate distortion, Remaining subset

中图分类号: 

  • TN919.8
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