無(wú)線多媒體傳感器網(wǎng)絡(luò)論文：基于無(wú)線多媒體傳感器網(wǎng)絡(luò)圖像分布式處理技術(shù)研究

1、無(wú)線多媒體傳感器網(wǎng)絡(luò)論文：基于無(wú)線多媒體傳感器網(wǎng)絡(luò)的圖像分布式處理技術(shù)的研究【中文摘要】無(wú)線多媒體傳感器網(wǎng)絡(luò)是在傳統(tǒng)的無(wú)線傳感器網(wǎng)絡(luò)基礎(chǔ)上引入音頻、視頻、圖像、聲音等大容量信息的新型傳感器網(wǎng)絡(luò),在智能家居、戰(zhàn)場(chǎng)監(jiān)控、交通監(jiān)控等領(lǐng)域具有廣闊的應(yīng)用前景。無(wú)線多媒體傳感器網(wǎng)絡(luò)將傳統(tǒng)傳感器網(wǎng)路的自組織、無(wú)人值守等優(yōu)點(diǎn)和新興多媒體技術(shù)感知媒體豐富的特點(diǎn)有機(jī)結(jié)合起來(lái),既具有傳統(tǒng)傳感器網(wǎng)絡(luò)(WSNs)的自組織、多跳路由、資源受限等共同特點(diǎn),又在能耗分布、服務(wù)質(zhì)量(QoS)要求、網(wǎng)絡(luò)安全、傳感器模型等方面具有顯著的個(gè)性化特點(diǎn)。由于無(wú)線多媒體傳感器網(wǎng)絡(luò)中節(jié)點(diǎn)配備電池供電和低功耗的處理芯片,它的能量、處理能力、存

2、儲(chǔ)能力、通信能力等都十分受限,使得能量的高效利用成為無(wú)線傳感器網(wǎng)絡(luò)的首要目標(biāo)。在無(wú)線多媒體傳感器網(wǎng)絡(luò)中圖像信息提供更精確豐富的監(jiān)測(cè)信息,但是信息量巨大,因此如何有效的壓縮和傳輸圖像至關(guān)重要。在無(wú)線多媒體傳感器網(wǎng)絡(luò)(WMSNs)中,通常使用數(shù)據(jù)壓縮編碼、冗余信息過(guò)濾以及數(shù)據(jù)融合等處理技術(shù)減少數(shù)據(jù)的傳輸量,考慮到WMSNs單個(gè)節(jié)點(diǎn)能量有限和處理能力有限的特點(diǎn),可以利用多個(gè)節(jié)點(diǎn)的協(xié)同處理能力,通過(guò)“在網(wǎng)計(jì)算”完成數(shù)據(jù)壓縮編碼等處理,將冗余度低、數(shù)據(jù)量小、信息量足的處理結(jié)果逐級(jí)上傳至控制中心。針對(duì)WMSNs中圖像處理的特殊要求,本文在研究WMSNs的分布式圖像壓縮算法的基礎(chǔ)上,對(duì)已有的算法改進(jìn),提出了

3、一種適用于WMSNs的自適應(yīng)分布式圖像壓縮算法；此外對(duì)當(dāng)前的分布式視頻編碼技術(shù)進(jìn)行了研究,探討了其在圖像壓縮方面的應(yīng)用。首先考慮到Walsh-Hadamard變換簡(jiǎn)單高效和能量集中的特點(diǎn),可以將該變換算法應(yīng)用于圖像壓縮算法中。由于Walsh變換采用二值正交基,非常適合計(jì)算機(jī)實(shí)現(xiàn),而且有相應(yīng)的快速計(jì)算機(jī)算法實(shí)現(xiàn)。針對(duì)WMSNs節(jié)點(diǎn)處理能力有限的特點(diǎn),這種變換較DCT變換、小波變換需要更少的存儲(chǔ)空間,提高了運(yùn)算速度。仿真實(shí)驗(yàn)結(jié)果表明采用Walsh-Hadamard變換的算法與基于DCT變換算法相比更加簡(jiǎn)單快速。其次考慮到WMSNs無(wú)線信道的特點(diǎn),提出一種基于信道估計(jì)的自適應(yīng)圖像壓縮機(jī)制。無(wú)線信道具

4、有信號(hào)衰減,延遲大,高誤碼率突發(fā)數(shù)據(jù)傳輸?shù)忍攸c(diǎn),而多媒體信息處理需要Qos保障,如延遲容忍度,數(shù)據(jù)傳輸所需帶寬。可以基于信道狀態(tài)的準(zhǔn)確估計(jì),自適應(yīng)調(diào)整量化表,在保證圖像質(zhì)量滿足應(yīng)用的前提下,獲得更高的壓縮比率。最后對(duì)分布式信源編碼理論和實(shí)用編碼技術(shù)進(jìn)行了研究,針對(duì)WMSNs的應(yīng)用進(jìn)行了討論,探討了其在圖像壓縮方面的應(yīng)用。【英文摘要】WMSNs is on the basis of WSNs with introducing audio, video, image,sound, large-capacity information.Its widely used in environmental

5、 monitoring, battlefield surveillance, traffic monitoring,ect.Besides self-organizing,unattended it also combines perception of media-rich multimedia technology. On the one hand,it is traditional self-organizing,multi-hop routing, resource constraints and other common characteristics.On the other ha

6、nd,the energy distribution,QoS requirments,sensor model and also has a significant personal characteristics.As energy of nodes, processing power,storage capacity of WMSNs isvery limited,efficient use of energy is the primary objective of WMSNs.The compression and transmission of image is critical.Co

7、mpression,filtering,and data fusion of information processing technology are widely used in WSNs. Taking account of the limited energy of single node,processing power,we can use the processing power of multiple nodes, with “in network computing” we can complete the compression of multimedia informat

8、ion and upload the small amount of data with sufficient information to the next target step by step.Because of the special requirements,based on the improvement of existing algorithms,a distributed image compression algorithm is proposed.First because the Walsh-Hadamard transform is simple,efficient

9、 and enengy focused,so it can be applied to the image compression algorithm.lt is very suitable for computer implementation.Compared to DCT transfom and wavelet transform,it requires less storage space and improves the speed of operation.The simulation results show that the algorithm with Walsh-Hada

10、mard transform is more simple and rapid than DCT-based algorithm.Secondly taking account of the characteristics of wireless channel in WSNs,we propose an adaptive image compression mechanism with channel estimation.Bacause wireless channel is signal-attenuation,large delay,high bit err rate and burs

11、t data transmission,multimedia information processing needs proper Qos,such as delay tolerance,data bandwidth,ect.Based on accurate channel state estimation,we can use adaptive quantization table, ensure that the quality of image meet the needs and get higher compression ratio.Simulation results sho

12、w that the algorithm in intensive WMSNs can prolong the network life cycle.Finally according to the previous improvement we proposed the adaptive distributed image compression algorithm with Walsh-Hadamard transform.In the transform stage, the Walsh-hadamard tranfrom is used to reduce the node proce

13、ssing overhead. In the quantitative phase,we use the channel estimation mechanism to quantilize adaptively and make higher compression ratio.The simulation results show that the proposed algorithm with limited resources and deployment of intensive WMSNs nodes,it prolongs the network lifetime signifi

14、cantly.【關(guān)鍵詞】無(wú)線多媒體傳感器網(wǎng)絡(luò) Walsh-Hadamard變換 DCT變換 自適應(yīng)圖像壓縮 分布式【英文關(guān)鍵詞】wireless multimedia sensor network Walsh-Hadamard transform DCT transform adaptive image compression distributed compression【目錄】基于無(wú)線多媒體傳感器網(wǎng)絡(luò)的圖像分布式處理技術(shù)的研究摘要10-12ABSTRACT12-13第一章 緒論14-191.1 研究背景14-151.2 研究的目的和意義15-161.3 國(guó)內(nèi)外研究現(xiàn)狀16-181.4 本文

15、組織結(jié)構(gòu)18-19第二章 無(wú)線多媒體傳感器網(wǎng)絡(luò)綜述19-322.1 無(wú)線多媒體傳感器網(wǎng)絡(luò)概述19-232.1.1 無(wú)線多媒體傳感器網(wǎng)絡(luò)的概念19-202.1.2 無(wú)線多媒體傳感器網(wǎng)絡(luò)的特點(diǎn)20-222.1.3 無(wú)線多媒體傳感器網(wǎng)絡(luò)的應(yīng)用22-232.2 無(wú)線多媒體傳感器網(wǎng)絡(luò)的關(guān)鍵技術(shù)及其研究進(jìn)展23-312.2.1 節(jié)點(diǎn)系統(tǒng)23-242.2.2 MAC協(xié)議24-262.2.3 路由協(xié)議26-282.2.4 多媒體信息處理28-292.2.5 其它關(guān)鍵技術(shù)29-312.3 本章小結(jié)31-32第三章 基于Walsh-Hadamard變換的分布式圖像壓縮算法32-453.1 概述32-333.1.1

16、 無(wú)線多媒體傳感器網(wǎng)絡(luò)圖像處理面臨的問(wèn)題323.1.2 有的解決方案及其局限性32-333.2 Walsh-Hadamard變換33-363.2.1 Walsh-Hadamard變換的概念33-343.2.2 Walsh-Hadamard變換的特點(diǎn)34-353.2.3 Walsh-Hadamard變換應(yīng)用于圖像壓縮35-363.3 基于Walsh-Hadamard變換的分布式圖像壓縮算法36-413.3.1 系統(tǒng)模型36-373.3.2 基于Walsh-Hadamard變換的分布式圖像壓縮算法37-383.3.3 算法性能仿真38-413.4 基于Walsh變換的分布式圖像壓縮算法在tinyo

17、s的設(shè)計(jì)與實(shí)現(xiàn)41-433.4.1 Tinyos簡(jiǎn)介41-423.4.2 基于walsh變換的圖像壓縮算法的概要設(shè)計(jì)42-433.5 本章小結(jié)43-45第四章 一種基于信道估計(jì)的自適應(yīng)量化機(jī)制45-514.1 概述454.2 無(wú)線信道模型45-464.2.1 無(wú)線信道的特點(diǎn)454.2.2 無(wú)線信道模型45-464.3 一種基于信道估計(jì)的自適應(yīng)量化機(jī)制46-494.3.1 自適應(yīng)量化機(jī)制46-474.3.2 仿真驗(yàn)證47-494.4 自適應(yīng)量化機(jī)制在tinyos操作系統(tǒng)下的概要設(shè)計(jì)49-504.4.1 協(xié)議棧模型設(shè)計(jì)494.4.2 信道評(píng)價(jià)器組件接口設(shè)計(jì)49-504.5 本章小結(jié)50-51第五章 分布式信源編碼理論與實(shí)用編碼技術(shù)研究51-605.1