WMSN节点的低内存开销图像压缩方法  

Image Compression Method with Low Memory Cost on Sensor Nodes of WMSN

作　　者：陆明洲[1] 沈明霞[1] 刘志强[1] 刘龙申[1] 杨晓静[2] 王宇[3] 

机构地区：[1]南京农业大学江苏省现代设施农业技术与装备工程实验室,南京210031 [2]南京农业大学农业部动物生理生化重点实验室,南京210095 [3]东南大学信息科学与工程学院,南京211189

出　　处：《农业机械学报》2014年第11期292-297,310,共7页Transactions of the Chinese Society for Agricultural Machinery

基　　金：农业部公益性行业科研专项资助项目(201003011);江苏省产学研联合创新资金资助项目(BY2012207);南京农业大学青年科技基金资助项目(KJ2011021)

摘　　要：基于比特平面及二值自适应算术编码提出了一种逐行小波系数编码方法,该方法能与低内存开销的逐行小波变换无缝、高效对接。处理器从图像节点FIFO通道中逐行读出图像信息,完成多级小波变换后,根据各层小波系数概率分布确定量化值,利用4个二值概率模型对系数各比特平面执行二值自适应算术编码,实现了基于小波变换的低内存开销图像压缩。利用该压缩方法处理一幅320像素×240像素仔猪灰度图像,结果表明,量化位数取3位时,存储开销、时间开销及峰值信噪比为5.749 KB、16.312 s及39.72 d B,内存开销低且重构图像质量较高。Based on bit plane and adaptive binary arithmetic coding, a wavelet coefficients coding scheme which could match the line-based wavelet transform efficiently was proposed. The MCU of the image sensor node read image data from FIFO line by line, executed multi-level wavelet transform, determined the quantization value according to the probability distribution of the wavelet coefficients in different level, performed adaptive binary arithmetic coding based on four binary probabilistic model and realized image compression based on wavelet transform with a low cost of SRAM. The image compression method was applied to handle a 320 pixels × 240 pixels gray image of piglets. Experimental results show that the SRAM cost, time cost and the PSNR was 5. 749 KB, 16. 312 s and 39.72 dB respectively when the quantization value was set to three. This study established the foundation for agricultural image transmission over the low bandwidth WMSN efficiently.

关 键 词：多媒体传感器网络 图像压缩 低内存系数编码 比特平面 二值算术编码 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置] TN911.7[自动化与计算机技术—控制科学与工程]