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%。
Compared with traditional image processing methods,the block compressive sensing can concurrently finish both acquisition and compression with a very low complexity,which will be an ideal choice for some wireless sensors with limited power.For block compressive sensing of any image,this paper proposes a cross subset-guided adaptive measurement method.The proposed method can adaptively allocate its sampling subrate to different regions,and also introduce the spatial prediction of mea-surement blocks,which effectively improves the quality of image reconstruction and the coding efficiency of measurement blocks.Specifically,starting from the center block in the spiral scanning order,all blocks of any image are divided into three regions:inner region,middle region,and outer region.Every few blocks of each region are put into a cross subset.Firstly,these blocks of each cross subset are measured by the same measurement matrix at a basic sampling subrate.Secondly,according to the cross-subset measurement values of three regions,their weights are used to assign different sampling subrates for the remaining subset.Thirdly,the remaining-subset blocks of the three regions are measured by different sampling subrates,which are proportional to their weights.For each measurement block,the optimal predictive block is found from the surrounding area of the measurement block,and the difference between them is quantized by scalar quantization.The experimental results show that compared with the exis-ting measurement methods,the proposed method not only improves the subjective quality of reconstructed image,but also improves the average rate-distortion performance of image reconstruction by at least 3.2%.
作者
田伟
刘浩
陈根龙
宫晓蕙
TIAN Wei;LIU Hao;CHEN Gen-long;GONG Xiao-hui(College of Information Science and Technology,Donghua University,Shanghai 201620,China;Key Laboratory of Artificial Intelligence,Ministry of Education,Shanghai 200240,China)
出处
《计算机科学》
CSCD
北大核心
2020年第12期190-196,共7页
Computer Science
基金
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。
关键词
分块压缩感知
自适应采样率
交叉子集
剩余子集
块预测
率失真
Block compressive sensing
Adaptive sampling subrate
Cross subset
Remaining subset
Block prediction
Rate distortion
