Research Article Preprocessing Techniques for High

Research ArticlePreprocessing Techniques for High-Efficiency Data Compressionin Wireless Multimedia Sensor Networks

Junho Park1 and Jaesoo Yoo2

1Agency for Defense Development Daejeon 305-600 Republic of Korea2School of Information and Communication Engineering Chungbuk National University CheongjuChungbuk 361-763 Republic of Korea

Correspondence should be addressed to Jaesoo Yoo yjschungbukackr

Received 29 August 2014 Accepted 3 November 2014

Academic Editor Seungmin Rho

Copyright copy 2015 J Park and J Yoo This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

We have proposed preprocessing techniques for high-efficiency data compression in wireless multimedia sensor networks Todo this we analyzed the characteristics of multimedia data under the environment of wireless multimedia sensor networks Theproposed preprocessing techniques consider the characteristics of sensed multimedia data to perform the first stage preprocessingby deleting the low priority bits that do not affect the image quality The second stage preprocessing is also performed for theundeleted high priority bits By performing these two-stage preprocessing techniques it is possible to reduce the multimediadata size in large To show the superiority of our techniques we simulated the existing multimedia data compression schemewithwithout our preprocessing techniques Our experimental results show that our proposed techniques increase compressionratio while reducing compression operations compared to the existing compression scheme without preprocessing techniques

1 Introduction

In recent with the development of hardware technologiesand monitoring schemes the applications for gatheringmultimedia data such as sound and image using multimediasensors have been increased [1 2] As the multimedia dataare very large over simple data in traditional sensor networksthe network lifetime of the sensor network is significantlyreduced due to excessive energy consumption in particularnodes for transmitting the multimedia data [3] In additionthe multimedia data increase the data transmission time anddecline the data reception ratio Consequently the existingschemes based on the traditional sensor networks are notsuitable for the environments to collect the multimedia data[3 4]

It is necessary to use compression schemes to alleviatesuch problems However most of the existing compressionschemes for sensor data are based on signal compression suchas wavelet and variable quantization and code compression[5]These studies are not suitable for the environments basedon wireless sensor networks The compression schemes for

wireless multimedia sensor networks are at an early stageConsidering this a novel compression scheme based on theChinese remainder theorem for themultimedia data has beenproposed in [6] The compression scheme based on the Chi-nese remainder theorem considered the characteristics andlimitation of wireless multimedia sensor networks Howeverthe existing compression scheme still suffers from energyconsumption in the wireless multimedia sensor network withthe limited energy

In this paper we propose preprocessing techniques forhigh-efficiency data compression in wireless multimediasensor networks The proposed techniques consider thecharacteristics of sensed multimedia data to perform the firststage preprocessing by deleting the low priority bits that donot affect the image quality The second stage preprocessingis also performed for the undeleted high priority bits Byperforming these two-stage preprocessing techniques it ispossible to reduce the multimedia data size in large

The remainder of this paper is organized as followsSection 2 overviews and analyzes the existing compres-sion schemes In Section 3 we present our preprocessing

Hindawi Publishing CorporationAdvances in MultimediaVolume 2015 Article ID 380849 7 pageshttpdxdoiorg1011552015380849

2 Advances in Multimedia

techniques for high-efficiency data compression in wirelessmultimedia sensor networks Section 4 shows the simulatedexperiments and compares the existing scheme with theproposed scheme Finally we present concluding remarks inSection 5

2 Related Works

The existing compression schemes for multimedia data canbe categorized under 1st and 2nd generation schemes [7]The1st generation schemes such as DCT based compression [8]EZW [9] SPIHT [10] and EBCOT [11] emphasize more onhow well the information contained in a transformed multi-media data is efficiently encoded whereas the 2nd generationschemes such as Pyramidal [12] Directional Decomposition[13] Segmentation [14] and Vector Quantization [15] placemore importance on how we can exploit and extract usefulinformation from themultimedia data However the existingcompression schemes require the high-computational powerand largememory to process the compression Because of theconstrained hardware of a wireless sensor node these studiesare not suitable for the environments based onwireless sensornetworks Considering this a compression scheme based onthe Chinese remainder theorem for the multimedia data wasproposed [6] However the existing compression schemesuffers from energy consumption in the wireless multimediasensor network with the limited energy Therefore it isnecessary to study a preprocessing technique considering thecharacteristics of the wireless multimedia sensor networks toimprove the efficiency of compression scheme

3 The Preprocessing Techniques forHigh-Efficiency Data Compression

In this paper for the maximization of compression effi-ciency we propose the preprocessing techniques that consistof dynamic area extraction and bit plane deletion beforeconducting compression scheme By doing so the proposedscheme reduces data compression operation improves com-pression ratio andminimizes energy consumption generatedfrom data transmission by transmitting the remaining datainstead of original multimedia sensor data

31 Preprocessing Technique (1) Extracting the Dynamic AreaIn general the wireless multimedia sensor networks requirethe installation costs and distribute the few multimediasensor nodes and many general sensor nodes together Asshown in Figure 1 the first detection is done through generalscalar sensor nodes When an abnormal signal (event) isdetected the near multimedia sensor nodes perform thesecond detection for the detailed monitoring The positionof a sensor node distributed to the network is fixed As themultimedia sensor node is designed for the unidirectionalshooting it continuously shoots the image data of the space sothat the abnormal signal is detected by adjusting the shootingangle and transmits the sensed data to the base station

Figure 2 shows the characteristics of sensed imagesin the wireless multimedia sensor networks As shown in

Scalar sensor nodes

Multimedia sensor nodes

Base station

Sensing fields

Figure 1 Deployment of wireless multimedia sensor networks

the Figure 2 the wireless multimedia sensor node contin-uously shoots the image of space that the abnormal signalis detected so that a dynamic area and a static area with aconsiderable size occur according to the time By consideringthe characteristics the transmission of the whole senseddata causes the unnecessary communication cost It alsoreduces network lifetime In order to solve such problems theproposed scheme extracts and compresses the high efficientdynamic area

First of all it is important to detect the dynamic areain the sensed data As it needs a high arithmetic operationcost to compare all pixels in the detection stage of thedynamic area it is not efficient Therefore the proposedscheme generates the virtual comparison block through thepixel clustering and the detection of the image change anduses it as the transmission unit as shown in Figure 3 Asthe virtual comparison block is utilized as a transmissionunit there should be no effect on the compression based onthe Chinese remainder theorem [3] The conditions of thevirtual comparison block are as follows by the definitionof compression scheme based on the Chinese remaindertheorem

Condition 1 As the continuous decimal numbers to utilizethe Chinese remainder theorem are at least two or more itmust be possible to express the comparison block data withtwo continuous decimal numbers

Condition 2 As the actual transmission data to the basestation is the remainder the remainder is below themaximum expression range of a specific variable type for themaximum efficiency

Condition 3 For the easy restoration of original data andblock data it is a structure of square which is applicable forthe QCIF format

By the conditions above the maximum block size forpossible utilization is 3 times 3 pixels (the proof is omitted dueto the space limit)

Advances in Multimedia 3

(a) 1199050

Static area

Dynamic area

(b) 1199051

Figure 2 Characteristic of sensed images in the wireless multimedia sensor networks

0 1 2

3 4 5

6 7 8

(a) Virtual comparisonblock n

(b) Average value of vir-tual comparison block n

Figure 3 Virtual comparison block and clustering operation

The virtual comparison block with a grid shape is gen-erated to the sensed image As in (1) the proposed schemeclusters the colors of all pixels inside the correspondingvirtual comparison block and computes their average valuesAt this time if the average values of the colors in the virtualcomparison blocks of the initially sensed images and thecurrently sensed images are above the threshold value theyshould be recognized as the dynamic areas as follows1003816100381610038161003816firstImgBlock navrValue minus curImgBlock navrValue100381610038161003816

1003816

gt 120572

(1)

Figure 4 shows the detection of the dynamic area and theextraction of the transmission areaThrough the comparisonof average values of colors in the virtual comparison blockthe dynamic area that must be transmitted is recognizedThe corresponding dynamic area is compressed based onthe Chinese remainder theorem to transmit it to the basestation At this time when the packets are transmitted inthe virtual comparison block unit unnecessary data trans-mission occurs due to header and trailer To solve such aproblem the proposed scheme transmits the dynamic area ina single packet through the establishment and extraction ofthe maximum transmission area Through this process theproposed scheme minimizes the energy consumption by thepartial compression and transmission of the dynamic area

32 Preprocessing Technique (2) Deleting Bit Plane DataThe sensor node is driven based on the limited energyand computing performance so that it needs an additional

compressionmodule to perform the data compression How-ever it requires a lot of installation costs Considering thistransmitting the original data was common in the existingscheme Figure 5 shows the structure of multimedia sensordata in wireless multimedia sensor networks The image datasensed from the wireless multimedia sensor network has adata bit structure of the pixel unit and each pixel has eightbits (0sim255) per color channel (R G B) as shown in Figure 5The same bits are bound together to compose a bit planeAs a result the color image is composed of 24 bit planes intotal While the data sizes of the bit plane are equivalent theamount of their expressible information and the importanceare different For example each bit of themost significant 7 bitplane can express 128 but each bit of the lowest significant0 bit plane can express only 1 In other words the mostsignificant bit plane shows a big color change and the lowestbit plane shows the small color change

Based on this analysis Figure 6 shows the changes inimage quality according to the bit plane deletion When themost significant bit plane is deleted serious damages to thequality of the original data occur However when the lowsignificant bit plane data is deleted it does not largely affectthe quality of the original data From the result when all bitplane data cannot be transmitted the high quality image canbe maintained by deleting data near the lowest bit plane Inthe proposed scheme the preprocessing (2) is performed forthe image data based on this fact

According to the result from the preprocessing (2) fewbit plane data are selected as transmission data As a resultthe proposed schememinimizes the energy consumption andincreases the lifetime of awhole network by utilizing this kindof preprocessing techniques

4 Performance Evaluation

We have developed a simulator based on JAVA to evalu-ate the existing multimedia data compression scheme [6]withwithout our preprocessing techniques This simulationwas carried out by constructing the performance evaluationenvironment shown in Table 1

In order to carry out performance evaluation we utilizedthe uncompressed MPEG-4 standard image provided byXiphorg [16] Figure 7 shows the sample images of uncom-pressed MPEG-4 standard images This simulation was done


(a) 1199050

First pixel ofdynamic area

Dynamic area

Last pixel ofdynamic area

(b) 1199051

Figure 4 Detection of the dynamic area and extraction of the transmission area

Bit-plane 0Bit-plane 1Bit-plane 2

BB

Bit-plane 3

Bit planBitBBit-Bit-BiBit-Bit-Bit-Bit-Bit-Bit-Bit-Bit plaplplaplaplaaaplanplaaplaplplplp eBB

Bit-plane 4

ananananlanlanlanlananananeeeeeeeee 00000000000Bit planBitBitBit-it-Bit-Bit-Bit-Bit-Bit-Bit-Bit-Bit-plaplaplaplaplaplaplaplanplaplapp eBBB

Bit-plane 5

annnnanlanlanlanlananananeeeeeeeeeeee 1111111111Bit planit plan

BBit-BitBit-Bit-BiBit-Bit-Bit-Bit-Bit-Bit-Bit-plaplaplaplaplaplaplanplaplaplplp eBBB

Bit-plane 6

nananlanlanlannlananananeeeeeeeeeee 2222222222B planit plan

Bit-BitBit-BitBit-Bit-Bit-Bit-Bit-Bit-Bit-Bit planplanplannplanplanplanplanplanplanplanplaneeeeeeeeeee 33333333333Bit planBit plaBitBit-Bit-BitBit-Bit-Bit-Bit-Bit-Bit-Bititit

nplanaplanplanplaplanplanplanplanplanplanplanplaneeeeeee 4BBitBBBBBitB

Bit-plane 7

nnnnneeeeeeeee 44444444444planBit-BiBit-BitBit-Bit-Bit-Bit-Bit-Bit-Bit-Bit-planplplanplanplanplanplanplanplanplanplanplanplanplaneeeeeeeeeeee 555555555Bit plaBBit-Bit-BiBit-Bit-Bit-Bit-Bit-Bit-Bit-Bit-Bit

nplanplanplanplanplanplanplanplanplanaplanplanplaneeeeeeee 6666666666BBit-planBit-plane 7

MSB(most significant)

LSB(least significant)

128 64 32 16 8 4 2 1X(0)X(1)X(2)X(3)X(4)X(5)X(6)X(7)

Figure 5 Structure of multimedia sensor data in wireless multimedia sensor networks

Table 1 Simulation parameters

Parameters Values

Multimedia dataUncompressed MPEG-4

standard images provided byXiphorg and CCTV image

Length of multimedia data(frames) 50

Format of multimedia data(px times px)

CIF (352 times 288)QCIF (176 times 144)

targeting the image having CIF (full CIF) and QCIF (quarterCIF) resolutionwhich are generally gathered bywirelessmul-timedia sensor networks In Table 2 the image classificationindicates spatial complexity andmotion amount in which ldquoArdquois the image expressing a tiny level of spatial complexity andmovement while ldquoBrdquo signifies the image expressing a mid-level of spatial complexity and movement [17] In additionthe wireless multimedia sensor networks have a distributioncharacteristic andmonitoring characteristic similar to that ofclosed circuit TV (CCTV) Therefore in this simulation theperformance evaluation was conducted by collecting imagesof actual CCTVThe relevant images are CCTV images [18] ofthe fire occurrence at Busan Metro Daeti Station in Korea on

Table 2 Characteristics of uncompressedMPEG-4 standard images[16]

Resolution(px times px)

Size of images(Mbytes) Frames Class

(a) AkiyoCIF

(352times288) CIF (44)300 A

QCIF(176times144) QCIF (11)

(b) NewsCIF

(352times288) CIF (44)300 B


August 27 2012 and for simulation we converted the formatto CIF andQCIF Figure 8 shows the sample images of CCTVimages

Figure 9 shows the compression ratio per frame tostandard image and CCTV image of the proposed schemewith preprocessing techniques Performance evaluation wascarried out by grafting preprocessing technique (1) andpreprocessing technique (2) onto the existing scheme Pre-processing technique (1) indicates dynamic area extractionand preprocessing technique (2) 2 4 and 6 indicate


11111111 01111111 00111111

00011111 00001111 00000111

00000011 00000001 00000000

(a) Deletion of the most significant bit plane data

11111111 11111110 11111100

11111000 11110000 11100000

11000000 10000000 00000000

(b) Deletion of the least significant bit plane data

Figure 6 Changes in image quality according to the bit plane deletion

(a) Akiyo (b) News

Figure 7 Sample images of uncompressed MPEG-4 standard images

Figure 8 Sample images of CCTV image

the number deleting the bit plane respectively Accordingto the experimental result in case preprocessing technique(1) is applied the compression ratio improves as the areafor compression is reduced by extracting dynamic area whiletransmitting the relevant area by compressing the area onlyIn addition in case preprocessing technique (2) is appliedthe compression ratio also improves since the number of bitplanes for compression is reduced by adjusting the qualityof multimedia sensor data through the deletion of bit planesaccording to applications In case 1 bit plane is deletedpreprocessing technique (2) improves compression efficiencyby 125 on the basis of grayscale image but as much


00100200300400500600700800900

1000

CIF QCIF

Com

pres

sion

ratio

()

Proposed

Pre 1 + propPre 2 (2) + prop

Pre 2 (4) + propPre 2 (6) + prop

Pre 1 + pre 2 (2) + propPre 1 + pre 2 (4) + prop

Pre 1 + pre 2 (6) + prop

(a) Sample images

00100200300400500600700800900

1000

CIF QCIF

Com

pres

sion

ratio

()

Proposed





(b) CCTV image

Figure 9 Compression ratio per frame with preprocessing techniques

image information is lost therefore proper setup shouldbe done according to application In addition in the eventof grafting preprocessing technique (1) onto CCTV imageimage change severely comes up which causes dynamic areato be wide extracted relatively and consequently it does notserve to improve compression efficiency to a larger extentthan optimized image Nonetheless preprocessing technique(2) belongs to arithmetic operation deleting bit plane caus-ing no big impact As a result the existing scheme withpreprocessing technique (1) and the existing scheme withpreprocessing technique (2) improve the data compressionratio by about 25 and 429 respectively over the existingscheme without preprocessing techniques In addition whenpreprocessing technique (1) and preprocessing technique (2)were simultaneously grafted the existing scheme improvesthe data compression ratio by about 492 over the existingscheme without preprocessing techniques

Figure 10 shows the compression arithmetic operationquantity in the existing scheme with preprocessing tech-niques Preprocessing technique (1) requires per-pixel com-parison operation with the previous image in order to extractthe dynamic area making it inevitable to carry out large-scale arithmetic operation In order to reduce this per-block data comparison is performed but it also requireslarge-scale arithmetic operations In the event of graftingpreprocessing technique (2) the compression operationquantity is decreased which can be explained by the factthat compression operations decrease as much because thebit plane deleted by virtue of simple deletion operation doesnot require compression operations Preprocessing technique(2) gets improved in compression efficiency by 125 on thebasis of grayscale image when 1 bit plane is deleted Howeversince the loss of image information can occur it is importantto properly set up according to applications

0

20

40

60

80

100

120

140

160

Com

pres

sion

oper

atio

n qu

antit

y(u

nits

fram

e)

CIF QCIF

times103

Proposed





Figure 10 Compression operation quantity with preprocessingtechniques

5 Conclusion

In this paper we have proposed preprocessing techniquesfor high-efficiency data compression in wireless multimediasensor networks To do this we analyzed the characteris-tics of multimedia data under the environment of wirelessmultimedia sensor networksTheproposed scheme considersthe characteristics of sensed multimedia data to perform thefirst compression by deleting the low priority bits that do notaffect the image qualityThe second stage compression is also


performed based on the Chinese remainder theorem for theundeleted high priority bits By performing this two-stagecompression it is possible to reduce the multimedia datasize in large To show the superiority of our techniques wesimulated the existing multimedia data compression schemewithwithout our preprocessing techniques As a result it wasshown through performance evaluation that the proposedpreprocessing scheme significantly increased compressionratio while reducing compression operation comparing tothe existing compression schemes without preprocessingtechniques In the future work we plan to study the effectivetransmission scheme considering the characteristics of com-pression data

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by the MSIP (Ministry ofScience ICT and Future Planning) Korea under the ITRC(Information Technology Research Center) Support Pro-gram (NIPA-2014-H0301-14-1022) supervised by the NIPA(National IT Industry Promotion Agency) and the C-ITRC(Convergence Information Technology Research Center)Support Program (NIPA-2014-H0401-14-1007) supervised bythe NIPA (National IT Industry Promotion Agency) by theICT R amp D Program of MSIPIITP [14-824-09-001] Devel-opment of High Performance Visual Big Data Discov-ery Platform for Large-Scale Real Time Data Analysisand by the Ministry of Education (MOE) and NationalResearch Foundation of Korea (NRF) through the HumanResource Training Project for Regional Innovation (no2013H1B8A2032298)

References

[1] S M Aziz and D M Pham ldquoEnergy efficient image transmis-sion in wireless multimedia sensor networksrdquo IEEE Communi-cations Letters vol 17 no 6 pp 1084ndash1087 2013

[2] P Wang R Dai and I F Akyildiz ldquoA differential coding-based scheduling framework for wireless multimedia sensornetworksrdquo IEEE Transactions on Multimedia vol 15 no 3 pp684ndash697 2013

[3] I F Akyildiz T Melodia and K R Chowdhury ldquoA survey onwireless multimedia sensor networksrdquo Computer Networks vol51 no 4 pp 921ndash960 2007

[4] T Ma M Hempel D Peng and H Sharif ldquoA survey of energy-efficient compression and communication techniques formulti-media in resource constrained systemsrdquo IEEE CommunicationsSurveys and Tutorials vol 15 no 3 pp 963ndash972 2013

[5] WC Li LMAng and P S Kah ldquoSurvey of image compressionalgorithms in wireless sensor networksrdquo in Proceedings of theInternational Symposium on Information Technology (ITSimrsquo08) pp 1ndash9 August 2008

[6] H Lee J Park D Seong and J Yoo ldquoAn energy-efficient datacompression and transmission scheme in wireless multimedia

sensor networksrdquo Journal of KIISE InformationNetworking vol39 no 3 pp 258ndash266 2012

[7] L W Chew L-M Ang and K P Seng ldquoSurvey of image com-pression algorithms in wireless sensor networksrdquo in Proceedingsof the International Symposium on Information Technology(ITSim rsquo08) pp 1ndash9 IEEE Kuala Lumpur Malaysia August2008

[8] D Cruz T Ebrahimi J AskelofM Larsson andCChristopou-los ldquoCoding of still picturerdquo in Applications of Digital ImageProcessing vol 4115 of Proceedings of SPIE 2000

[9] J M Shapiro ldquoEmbedded image coding using zerotrees ofwavelet coefficientsrdquo IEEE Transactions on Signal Processingvol 41 no 12 pp 3445ndash3462 1993

[10] A Said and W A Pearlman ldquoA new fast and efficient imagecodec based on set partitioning in hierarchical treesrdquo IEEETransactions on Circuits and Systems for Video Technology vol6 no 3 pp 243ndash250 1996

[11] D Taubman ldquoHigh performance scalable image compressionwith EBCOTrdquo IEEE Transactions on Image Processing vol 9 no7 pp 1158ndash1170 2000

[12] P J Burt and E H Adelson ldquoThe Laplacian pyramid as a com-pact image coderdquo in Proceedings of the Korean Institute of Infor-mation Scientists and Engineers vol 31 pp 532ndash540 1983

[13] A Ikonomopoulos and M Kunt ldquoHigh compression imagecoding via directional filteringrdquo Signal Processing vol 8 no 2pp 179ndash203 1985

[14] L Ghouti A Bouridane and M K Ibrahim ldquoImage com-pression using texture modelingrdquo in Proceedings of the IEEEInternational Symposium on Circuits and Systems (ISCAS rsquo05)vol 3 pp 2313ndash2316 May 2005

[15] G F McLean ldquoVector quantization for texture classificationrdquoIEEE Transactions on Systems Man and Cybernetics vol 23 no3 pp 637ndash649 1993

[16] XiphOrg Foundation 2013 httpwwwxiphorg[17] Xiphorg Video Test Media 2013 httpmediaxiphorgvideo

derf[18] Fire Occurrence at BusanMetroDaeti Station in Korea onAugust

2720122012httpwwwyoutubecomwatchv=Q-4KbAFEiEI

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



techniques for high-efficiency data compression in wirelessmultimedia sensor networks Section 4 shows the simulatedexperiments and compares the existing scheme with theproposed scheme Finally we present concluding remarks inSection 5

2 Related Works

The existing compression schemes for multimedia data canbe categorized under 1st and 2nd generation schemes [7]The1st generation schemes such as DCT based compression [8]EZW [9] SPIHT [10] and EBCOT [11] emphasize more onhow well the information contained in a transformed multi-media data is efficiently encoded whereas the 2nd generationschemes such as Pyramidal [12] Directional Decomposition[13] Segmentation [14] and Vector Quantization [15] placemore importance on how we can exploit and extract usefulinformation from themultimedia data However the existingcompression schemes require the high-computational powerand largememory to process the compression Because of theconstrained hardware of a wireless sensor node these studiesare not suitable for the environments based onwireless sensornetworks Considering this a compression scheme based onthe Chinese remainder theorem for the multimedia data wasproposed [6] However the existing compression schemesuffers from energy consumption in the wireless multimediasensor network with the limited energy Therefore it isnecessary to study a preprocessing technique considering thecharacteristics of the wireless multimedia sensor networks toimprove the efficiency of compression scheme

3 The Preprocessing Techniques forHigh-Efficiency Data Compression

In this paper for the maximization of compression effi-ciency we propose the preprocessing techniques that consistof dynamic area extraction and bit plane deletion beforeconducting compression scheme By doing so the proposedscheme reduces data compression operation improves com-pression ratio andminimizes energy consumption generatedfrom data transmission by transmitting the remaining datainstead of original multimedia sensor data

31 Preprocessing Technique (1) Extracting the Dynamic AreaIn general the wireless multimedia sensor networks requirethe installation costs and distribute the few multimediasensor nodes and many general sensor nodes together Asshown in Figure 1 the first detection is done through generalscalar sensor nodes When an abnormal signal (event) isdetected the near multimedia sensor nodes perform thesecond detection for the detailed monitoring The positionof a sensor node distributed to the network is fixed As themultimedia sensor node is designed for the unidirectionalshooting it continuously shoots the image data of the space sothat the abnormal signal is detected by adjusting the shootingangle and transmits the sensed data to the base station

Figure 2 shows the characteristics of sensed imagesin the wireless multimedia sensor networks As shown in

Scalar sensor nodes

Multimedia sensor nodes

Base station

Sensing fields

Figure 1 Deployment of wireless multimedia sensor networks

the Figure 2 the wireless multimedia sensor node contin-uously shoots the image of space that the abnormal signalis detected so that a dynamic area and a static area with aconsiderable size occur according to the time By consideringthe characteristics the transmission of the whole senseddata causes the unnecessary communication cost It alsoreduces network lifetime In order to solve such problems theproposed scheme extracts and compresses the high efficientdynamic area

First of all it is important to detect the dynamic areain the sensed data As it needs a high arithmetic operationcost to compare all pixels in the detection stage of thedynamic area it is not efficient Therefore the proposedscheme generates the virtual comparison block through thepixel clustering and the detection of the image change anduses it as the transmission unit as shown in Figure 3 Asthe virtual comparison block is utilized as a transmissionunit there should be no effect on the compression based onthe Chinese remainder theorem [3] The conditions of thevirtual comparison block are as follows by the definitionof compression scheme based on the Chinese remaindertheorem

Condition 1 As the continuous decimal numbers to utilizethe Chinese remainder theorem are at least two or more itmust be possible to express the comparison block data withtwo continuous decimal numbers

Condition 2 As the actual transmission data to the basestation is the remainder the remainder is below themaximum expression range of a specific variable type for themaximum efficiency

Condition 3 For the easy restoration of original data andblock data it is a structure of square which is applicable forthe QCIF format

By the conditions above the maximum block size forpossible utilization is 3 times 3 pixels (the proof is omitted dueto the space limit)


(a) 1199050

Static area

Dynamic area

(b) 1199051


0 1 2

3 4 5

6 7 8





1003816

gt 120572

(1)










(a) 1199050


Dynamic area


(b) 1199051



BB

Bit-plane 3


Bit-plane 4


Bit-plane 5



Bit-plane 6




Bit-plane 7





128 64 32 16 8 4 2 1X(0)X(1)X(2)X(3)X(4)X(5)X(6)X(7)



Parameters Values










(a) AkiyoCIF

(352times288) CIF (44)300 A


(b) NewsCIF

(352times288) CIF (44)300 B





11111111 01111111 00111111

00011111 00001111 00000111

00000011 00000001 00000000


11111111 11111110 11111100

11111000 11110000 11100000

11000000 10000000 00000000



(a) Akiyo (b) News





00100200300400500600700800900

1000

CIF QCIF

Com

pres

sion

ratio

()

Proposed





(a) Sample images

00100200300400500600700800900

1000

CIF QCIF

Com

pres

sion

ratio

()

Proposed





(b) CCTV image




0

20

40

60

80

100

120

140

160

Com

pres

sion

oper

atio

n qu

antit

y(u

nits

fram

e)

CIF QCIF

times103

Proposed






5 Conclusion






Acknowledgments


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










(a) 1199050

Static area

Dynamic area

(b) 1199051


0 1 2

3 4 5

6 7 8





1003816

gt 120572

(1)










(a) 1199050


Dynamic area


(b) 1199051



BB

Bit-plane 3


Bit-plane 4


Bit-plane 5



Bit-plane 6




Bit-plane 7





128 64 32 16 8 4 2 1X(0)X(1)X(2)X(3)X(4)X(5)X(6)X(7)



Parameters Values










(a) AkiyoCIF

(352times288) CIF (44)300 A


(b) NewsCIF

(352times288) CIF (44)300 B





11111111 01111111 00111111

00011111 00001111 00000111

00000011 00000001 00000000


11111111 11111110 11111100

11111000 11110000 11100000

11000000 10000000 00000000



(a) Akiyo (b) News





00100200300400500600700800900

1000

CIF QCIF

Com

pres

sion

ratio

()

Proposed





(a) Sample images

00100200300400500600700800900

1000

CIF QCIF

Com

pres

sion

ratio

()

Proposed





(b) CCTV image




0

20

40

60

80

100

120

140

160

Com

pres

sion

oper

atio

n qu

antit

y(u

nits

fram

e)

CIF QCIF

times103

Proposed






5 Conclusion






Acknowledgments


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










(a) 1199050


Dynamic area


(b) 1199051



BB

Bit-plane 3


Bit-plane 4


Bit-plane 5



Bit-plane 6




Bit-plane 7





128 64 32 16 8 4 2 1X(0)X(1)X(2)X(3)X(4)X(5)X(6)X(7)



Parameters Values










(a) AkiyoCIF

(352times288) CIF (44)300 A


(b) NewsCIF

(352times288) CIF (44)300 B





11111111 01111111 00111111

00011111 00001111 00000111

00000011 00000001 00000000


11111111 11111110 11111100

11111000 11110000 11100000

11000000 10000000 00000000



(a) Akiyo (b) News





00100200300400500600700800900

1000

CIF QCIF

Com

pres

sion

ratio

()

Proposed





(a) Sample images

00100200300400500600700800900

1000

CIF QCIF

Com

pres

sion

ratio

()

Proposed





(b) CCTV image




0

20

40

60

80

100

120

140

160

Com

pres

sion

oper

atio

n qu

antit

y(u

nits

fram

e)

CIF QCIF

times103

Proposed






5 Conclusion






Acknowledgments


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










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11000000 10000000 00000000



(a) Akiyo (b) News





00100200300400500600700800900

1000

CIF QCIF

Com

pres

sion

ratio

()

Proposed





(a) Sample images

00100200300400500600700800900

1000

CIF QCIF

Com

pres

sion

ratio

()

Proposed





(b) CCTV image




0

20

40

60

80

100

120

140

160

Com

pres

sion

oper

atio

n qu

antit

y(u

nits

fram

e)

CIF QCIF

times103

Proposed






5 Conclusion






Acknowledgments


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










00100200300400500600700800900

1000

CIF QCIF

Com

pres

sion

ratio

()

Proposed





(a) Sample images

00100200300400500600700800900

1000

CIF QCIF

Com

pres

sion

ratio

()

Proposed





(b) CCTV image




0

20

40

60

80

100

120

140

160

Com

pres

sion

oper

atio

n qu

antit

y(u

nits

fram

e)

CIF QCIF

times103

Proposed






5 Conclusion






Acknowledgments


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













Acknowledgments


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









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