Energy-Efficient Transmission Scheme of JPEGImages over Visual Sensor Networks

Abdelhamid Mammeri∗ IEEE student member, Ahmed Khoumsi∗, Djemel Ziou† and Brahim Hadjou∗∗Department of Electrical and Computer Engineering

†Department of Computer ScienceUniversity of Sherbrooke, Quebec, Canada, J1K 2R1

Email: Firstname.Lastname@USherbrooke.ca

Abstract—With Visual Sensor Networks (VSN), designers mustrespect strict constraints on energy consumption, which makecompression standards, such as JPEG, not energy-beneficial toVSN. Our approach for tackling this constraint problem consistsin adapting JPEG by exploiting the DCT energy compactionproperty. This exploitation is performed by processing only aportion of each block of 8 × 8 DCT coefficients of the capturedimage. This approach induces two conflicting effects. Indeed,reducing the size of the portion of DCT block presents theadvantage of reducing the energy consumed for processing andtransmitting an image, but it also presents the drawback ofreducing the quality of the image received at the sink. We proposetwo methods to solve this conflict: a global method and a localmethod. In the global method, an optimal size is computed forall portions of DCT blocks of a whole image.

In the local method, the image is partitioned into sub-imagesof two categories: regions of interests (ROI) and tiles. ROIs areregions for which a good image quality is required at the sink,whereas tiles are regions for which a poor image quality istolerated. Each ROI is processed as a whole image for whicha (local) optimal size is computed for its portions of DCT blocks.While for tiles, the size is selected in respect to the user interestand the residual energy of a visual sensor, and is in generalsmaller than the optimal size attributed to ROIs. ROIs and tilesare processed and sent differently, in the sense that the abovementioned conflict does not exist in tiles since, by definition, apoor image quality of tiles is tolerated.

Another contribution of this paper is to process ROIs andtiles differently using the criterion of transmission reliability.More precisely, we propose a semi-reliable mechanism whichguarantees that ROIs are transmitted before and more reliablythan tiles. For this purpose, we use a model of priority queuingsystem. Our results are illustrated by several simulations.

I. INTRODUCTION

Visual Sensor Networks (VSN) are Wireless Sensor Net-works (WSN) that capture, process and transmit images usingan ad-hoc architecture. VSN are useful in many applications,such as vehicular traffic monitoring, video-surveillance andobject detection/tracking. For example, VSN can be helpfulfor managing and controlling the traffic in urban or isolatedareas. Another interesting application of VSN is the handlingof pavement conditions during winter in a cold region likeCanada or Northern Europe. Wireless visual sensors (VS) areplaced in relevant locations such that they compose a VSN.VS nodes capture and compress images of the pavement andtransmit them through the VSN to a Control & Decision Center(CDC), which can be considered as a sink in the context of

VSN. From the analysis (possibly automatic) of the receivedimages, the CDC decides of correct actions to be taken, suchas to dispatch a person to put salt on relevant locations of thepavement.

Image transmission over VSN has been the subject ofmany research works, for example [1], [2]. The authors in[1] studied the energy consumption and the image qualityin wireless video-surveillance networks, when retransmissionof corrupted packets is performed. They used JPEG withinteger DCT kernel as compression algorithm, instead of thecommonly used floating point DCT to minimize transmissioncosts and delays. The authors do not really adapt JPEG tothe energy requirement of VSN. The authors in [2] showedonly the feasibility of transmission of JPEG images overZigBee nodes, without further details. On the other hand,the exploitation of the 2D DCT energy compaction propertywas used in [3] and [4]. The authors in [3] investigated thisidea in centralized wireless multimedia networks which differfrom VSN requirements. This property was also used in [4]to design an energy-aware VLSI system for portable devicesto compress an image.

The idea of minimizing the processing and transmission en-ergy when executing JPEG in VSN was investigated in [5], [6],where the DCT energy compaction property is exploited. Thisexploitation is performed by processing only a portion of eachblock of 8×8 DCT coefficients. In [6] the selected coefficientsportion is squared, while in [5] it is triangular. Unfortunately,both selections are performed arbitrarily, and the authors donot propose any method for selecting the size (i.e., the numberof DCT coefficients) of the portion to be processed. This isa relevant issue since reducing this size has two conflictingeffects: on the one hand, it presents the desirable effect ofreducing the energy consumed for transmitting an image; andon the other hand, it has the undesirable effect of reductionthe quality of the image received by the sink. We propose twomethods for solving this “VS energy-image quality” conflict:a global method and a local method.

In the global method, a unique optimal size is computedfor all portions of DCT blocks of a captured image. Weassume that it is highly desirable that the image be receivedat the sink with a good image quality, while respecting theenergy constraints of VSN. We have therefore to solve atrade-off between “minimizing the number of processed DCT

coefficients” (and thus the consumed energy) and “maximizingthe quality of the received image at the sink”. To solve thistrade-off, we develop an Akaike Information Criterion (AIC)as a function of the size ρ of the selected portion of each blockof 8×8 DCT. The value of ρ that minimizes the AIC functionis the one that solves optimally this trade-off. This value istherefore called optimal size and denoted ρo.

In the local method, the image is partitioned into severalsub-images of two categories: Regions Of Interests (ROIs) andtiles. In our context, ROIs refer to regions for which a goodimage quality is required at the reception by the sink, whereastiles are regions for which a poor image quality is tolerated atthe reception. Each ROI is considered as a whole image forwhich we apply the above global method to solve locally thetrade-off “minimize energy-maximize image quality”. There-fore, an optimal local size ρl

o is computed for each ROI. Thelocal method processes tiles more simply than ROI, becausethe image quality is not an issue. Therefore, with tile there isno trade-off to solve and the objective becomes to minimizethe energy consumption. The local method selects the size oftiles in respect to the user interest and the VS residual energy,and it is smaller than the optimal size attributed to ROIs.

Another contribution of this paper is the proposition of amechanism where ROIs are sent before and more reliablythan tiles. Because it is more acceptable for a tile thanfor a ROI to be received with a delay or never received.For this purpose, we use a priority queuing model whereROIs are granted higher priority than tiles. This semi-reliablescheme is implemented at each VS node, where it necessitatessome collaborations with routing layer. A simplified routingmechanism is used to illustrate the efficiency of our work. Andlast but not least, our results are illustrated by simulations.

This paper is structured as follows. In Section II, we presentsome fundamentals related to JPEG and 2D DCT energycompaction property. Both methods, global and local, areintroduced in Section III. In Section IV, the gain in energyof the local method is explained. After that, in Section V, wepresent the semi-reliable encoding and transmitting scheme.The used network model is then introduced in Section VI. InSection VII, we illustrate our results by a set of simulations.We summarize and present future directions in Section VIII.

II. EXPLOITING THE DCT ENERGY COMPACTION

PROPERTY

Remind that the process of lossy JPEG consists mainly ofthe following stages: the target image is carved into smallerblocks of size 8 × 8 pixels; the discrete cosine transform(DCT) is then applied on each 8 × 8 block; the uniformquantization is performed on the transformed coefficients; thequantization result is reordered in zigzag way from lowerto higher frequencies; the runlength encoding (RLE) is thenapplied to reduce the length of the generated sequences andat the last stage, the (Huffman or arithmetic) entropy codingis applied [19].

It is obvious to note that JPEG compression itself generatesredundant data that might be compressed further, by exploiting

the DCT energy compaction property defined as follows.The DC coefficient and some low to middle frequencies ACcoefficients tend to monopolize most of the signal energy.Thus, many high-frequencies of AC coefficients can be dis-carded without much loss of information [7]. This propertyis used to reduce the number of basic operations neededat each stage of JPEG scheme, and hence minimizing theenergy dissipated by each node. We use the notion of ReducedBlock Size, which consists in processing only the upper-leftsquared or triangular portion whose side’s length is ρ of eachblock of 8 × 8 DCT coefficients of a given image (Fig. 1).We note that the triangular selection of DCT coefficients isperformed with respect to their rearrangement in zigzaggedorder from low to high frequencies. The squared and triangularselections were exploited respectively in [6], [5] to yield two“adapted versions” of JPEG called Squared JPEG (S-JPEG)and Triangular JPEG (T-JPEG), respectively. With S-JPEG,we process only a squared portion of size (i.e., side’s length)ρ containing ρ2(< 64) coefficients (see Fig. 1.a). Whereaswith T-JPEG, we process a triangular portion of size ρ < 8and thus containing Cρ = ρ(ρ+1)

2 (< 64) coefficients (see Fig.1.b). Note that the standard JPEG processes the whole 8 × 8DCT block consisting of 64 coefficients.

Authors of [6], [5] present the idea of using a (squared ortriangular) portion of size ρ, but they do not consider how thevalue of ρ is selected. In Sections III-A and III-B, we proposerespectively a global method and a local method for selectingan optimal value of ρ in case of triangular selection, since it isless energy consuming than squared selection [5]. The squaredapproach is not investigated for space limitation.

Fig. 1. Squared and Triangular selection of DCT coefficients (ρ = 4)

III. OPTIMAL SELECTION OF DCT COEFFICIENTS

The approach of selecting a reduced portion of DCT blockhas two conflicting effects: on the one hand, it presentsthe desirable effect of minimizing the energy consumed forprocessing and transmitting an image; and on the other hand,it has the undesirable effect of reducing the quality of theimage received by the sink. This makes the selection of ρnot trivial. More precisely, decreasing ρ improves the energyconsumption (by reducing the size of the reduced portion) butdeteriorates the image quality at the reception by sink. On thecontrary, increasing ρ augments the energy consumption andimproves the image quality. Therefore, we have to solve thetrade-off between “minimizing the number of processed DCTcoefficients” (and thus the consumed energy) and “maximizing

the quality of the image received at the sink”. Depending onthe user interest, two methods are suggested, global methodand local method, which are explained in Sections III-A andIII-B, respectively.

A. Global method

In this section, we propose a global method which solvesthe above mentioned trade-off by computing a unique optimalsize denoted ρo for all portions of DCT blocks of a givenimage. We refer to this method as global, since we compute asingle ρo for the whole image. For that purpose, we developan algorithm based on AIC model [8], that automaticallyestimates the optimal number of DCT coefficients inside eachblock that solves the trade-off discussed before. The principleof AIC is that the optimum number of DCT coefficientsis computed by minimizing a so-called AIC function. Byadapting the general AIC form of [8] to our case, and sinceDCT coefficients follow the Gaussian distribution [9], we usethe following general AIC function:

AIC (Cρ) = 2Cρ + w log(Dw

) (1)

Where w is the number of observations of each coefficientDCT, and in our case, is represented by the number ofblocks inside one image; and D is the difference betweenthe “optimized” reconstructed image fo(x, y) and the originalimage f(x, y) having N × N pixels (x, y), and is given by:

D =N∑

x=0

N∑

y=0

[f(x, y) − fo(x, y)]2 (2)

In Eq. 1, Cρ is the number of processed and transmittedcoefficients, and the objective is to find the optimal value of ρwhich minimizes Eq. 1. Cρ is equal to ρ(ρ+1)

2 and it increaseswith the consumed energy. The second term of Eq. 1 reflectsthe difference between the original image and the receivedimage (which is inversely proportional to PSNR), and thus,decreases with the quality of the received image. Therefore,it is desirable to minimize these two terms. But actually,decreasing one of them increases the other. This correspondsto the trade-off already mentioned. AIC solves this trade-off bytargeting the objective to minimize the sum of the two terms.Therefore, the value ρ which will be selected is the one thatminimizes the AIC(Cρ).

In Eq. 1, w = (Nk )2 is the number of blocks inside one

image of size N × N . If we replace w by its value (Nk )2 in

Eq. 1 we obtain:

AIC(ρ) = 2Cρ + (N

k)2 log[(

k

N)2D] (3)

ρo is the value that minimizes Eq. 3, and is computed asfollows. For each ρ = 1 . . . k, we compute Cρ and D fromwhich we deduce AIC(ρ) by using Eq. 3. After that, weselect the value ρo that minimizes the AIC(ρ) value, whichwe note ρo = ArgminρAIC(ρ). In Section VII, we presentsome examples of finding ρo.

B. Local method

In the previous section, we have proposed a global methodthat computes ρo for optimizing the trade-off “minimizeenergy-maximize image quality”. But this computation had torespect the following requirement: the same ρo is applied toall DCT blocks of an image. The only justification that can befound to this requirement is that it simplifies the procedure. Aswe will explain, removing this requirement permits to improvethe method, in the sense that for the same energy consumptionwe can obtain a better image quality at the reception.

Let us now remove the above requirement as follows. Eachcaptured image is partitioned by the source node into severalrectangular and non-overlapping regions of two categoriesof Regions of Interest (ROI) and Tiles, which are regions ofhigh and low importance, respectively. Hence, one image mayconsist of several ROIs and tiles, which will be processedand sent differently. In our context, ROI reflects the userinterest within the physical monitored zone, while tiles areless interesting, but they might contribute to the understandingof the captured image. The union of all ROIs and tiles givethe decomposed image. As an illustrative example, Figure 2presents the image of Lena which is partitioned into one ROI(Lena face) and four tiles (T1 to T4).

Fig. 2. Regions Of Interest and Tiles

ROI is a region of high importance for which a goodimage quality is required at the sink, while respecting theenergy constraints of VSN. We have thus the same trade-off“minimize energy-maximize image quality”, which was solvedin Section III-A by using a global method. Now, we considerseparately each ROI as a whole image for which we applythe global method to solve locally the trade-off. Therefore,an optimal size ρl

o is computed for each ROI using Eq. 3 byreplacing the image size by the ROI size.

The local method processes tiles more simply than ROI,because the image quality is not an issue. Therefore, there isno trade-off to solve, and the objective becomes to minimizethe energy consumption. Since the image quality of a tile is notan issue, it is natural that the ρ selected for any tile must takeinto account the residual energy of the VS node and be smallerthan: 1) the optimal global ρo of the whole image (obtained

with the global method), and 2) the smallest of the optimallocal ρl

o’s of all the ROIs (obtained by the local method),the extreme case being taking ρl

o = 0 (i.e., not transmittingthe tile). We have to note that the residual energy of all VSnodes can be easily recognized by the sink, for example, bybroadcasting a special message by all nodes every X minutes(or secondes) towards the sink.

The following scenario explains the reason behind the imagedecomposition into tiles and ROIs. We suppose that the userat the sink is only interested by some area within a monitoredzone of interest, which can be represented by one or moreROIs within the captured image. Instead of compressing (usingthe fully JPEG) and transmitting the whole compressed data,which is energy consuming, the user at the sink sends aquery carrying the ROIs coordinates. The rest of the image,consisting of tiles in our context, can be encoded with asmall or null value of ρ (using T-JPEG). In order to illustratethe difference between the local and global methods, let usconsider the image of Fig. 2. If we use the global method withT-JPEG, we compute a unique optimal ρo = 6 for the wholeimage. Fig. 3 represents the image received by the sink. We seethat the image quality is uniform in the whole picture. If weuse the local method with T-JPEG, we compute an optimalρl

o = 6 for the ROI, and we take ρ = 1 for the four tiles.Fig. 4 represents the image received by the sink. While forenergy purpose, we will see in Sections IV and VII, how thelocal method improves energetically the global method. Wehighlight that the price to be paid for consuming less energyis a poorer image quality for tiles, which is not a problemsince tiles are irrelevant for the user.

Fig. 3. Image reconstruction using global method. The same ρo = 6 is usedfor the whole image

Furthermore, to give more sense to our scheme, ROIs(carrying the interesting information) are transmitted reliably,while tiles are sent unreliably, which further minimizes theconsumed energy. This “semi-reliable” scheme is introducedin Section V. By computation, we show in the next Sectionthe energy-efficiency of the local approach compared to theglobal approach.

Fig. 4. Image reconstruction using local method. Tiles are assigned ρ = 1,while the ROI (lena Face) is assigned ρl

o = 6

IV. ENERGY GAIN

To simplify the reader task’s, we use the notation T-JPEG(ρ)to mean that T-JPEG is used for a given value ρ, for exampleT-JPEG(5) means that T-JPEG is used for ρ = 5. Along thisarticle, we refer to ROIs and tiles as sub-images, and thecaptured image as original image, which is decomposed bya source node into a set of sub-images.

To show the energy efficiency of using the local method (ofSection III-B) instead of the global method (of Section III-A),we compare the energy consumed in the local method withthat one consumed in the global method. We use the high-level power consumption model developed in [5] to roughlyevaluate the processing energy consumed by one VS nodewhile executing JPEG or its adapted version (T-JPEG(ρ)). Inour previous work [5], all energy computations are performedfor JPEG and T-JPEG using the same ρ ≤ 8 for the wholeimage. The latter model can be applied to the global methodby taking the optimal value ρo computed in Section III-A. Inthis section we consider the case of the local method, i.e., theoriginal image is divided into sub-images (tiles and ROIs).The processing energy Ep consumed by one VS node whilecompressing an image of size N × N using JPEG is [5]:

Ep = Edct + Eq + Ez + Erle + Ehuf (4)

Where Edct, Eq, Ez , Erle and Ehuf represent the energiesconsumed at 2D DCT, quantizing, zigzagging, RLE and Huff-man (entropy) encoding stages, respectively using JPEG. Theenergy E�

p consumed by one node while executing T-JPEGusing any ρ ≤ 8 for the whole image is [5]:

E�p = E�

dct + E�q + E�

z + E�rle + E�

huf (5)

Where E�dct, E�

q , E�z , E�

rle, and E�huf are the energies con-

sumed at 2D DCT, quantizing, zigzagging, RLE and Huffmanstages, respectively, for T-JPEG using any ρ ≤ 8 for the wholeimage. The computations of all these energies are presented in[5]. We can use this model to compute the energy consumptionof the global method, by taking the optimal ρl

o.

Let us consider the energy computation of the local method.Let N × N be the size of a complete image, and K × Lbe the size of a given tile or ROI. We can adapt simply theenergy model of [5] to compute the energy consumption ofeach tile or ROI, as follows. Each tile or ROI is consideredas a separate sub-image of size K × L. For a tile, after theselection of ρ < ρl

o, we can adapt the model of T-JPEG of [5]by replacing the size N ×N by K ×L. As for ROI, the onlydifference with a tile is that the model of T-JPEG of K × Lis applied for the optimal ρl

o instead of ρ.Recall that the ρ selected for any tile is typically smaller

than the optimal global ρo of the whole image (obtainedusing the global method), and also smaller than the smallestof the optimal local ρl

o’s of all the ROIs (obtained by thelocal method). Recall also that reducing ρ induces a reductionof energy consumption. From this observation, we can easilydeduce that for the same image, the energy consumed usingthe global method is greater than the energy consumed withthe local method. This gain in energy will be quantified inSection VII by simulations.

V. SEMI-RELIABLE ENCODING AND TRANSMITTING

SCHEME

In this section, we consider the local method and propose ascheme for encoding and transmitting differently sub-images.Each sub-image is processed and packetized locally by asource node in the same way as with the original image,i.e., the same JPEG processing policies are applied for eachsub-image separately. Once a user at the sink chooses ROIswithin the original image, he sends a query to the visualsource node which, after taking a photo, divides the originalimage into a set of sub-images based onto meta-data carriedout by the sink query’s, and finally sends ROIs in a reliablemanner and tiles in an unreliable way. As said in Section III-B,for each ROI we compute ρl

o (Eq. 3), whereas for tiles, weattribute a pre-selected value of ρ (the choice of ρ dependson the user interest and the VS residual energy), in orderto gain more energy taking advantage of the fact that, bydefinition, a poor image quality is tolerated for tiles. Then,each ROI or tile are encoded using T-JPEG(ρl

o) for ROIs or T-JPEG(ρ) for tiles, and packetized into different priorities. Moreprecisely, a high priority is associated to ROIs packets and alow priority to tiles packets. Then a priority queuing system isused by the source and intermediate nodes to send ROI packetsbefore and more reliably than tile packets. Our approach issemi-reliable in the sense that only ROI packets are handledreliably (using NACK-based scheme), whereas tile packets arehandled unreliably (without acknowledgement). This choice ismotivated by the scarce resources in the context of VSN, andhence we can save more energy with (irrelevant) tiles.

A. Packet header format: VSN header

To implement our reliable scheme, we insert a simple VSNheader into JPEG header. The latter regroups all data relatedto a compressed image such as, quantification tables, Huffmanencoding tables, image size information, etc. [19]. VSN header

is inserted into the JPEG header, between each Scan headerand Entropy-coded segment (ECS) (see Figure 5). VSN headercomprises the following attributes (see Figure 6):

• SI: Sub-Image nature which can be ROI (SI = 0) or Tile(SI = 1). SI is used by a Differentiator at each entrynode to distinguish ROIs from tiles.

• N, Nt: Nt is the total number of packets within a sub-image and N is the packet sequence number, since eachpacket is labeled for counting purposes. For reliabilityconsiderations, N and Nt are used by a Checker to checkthe non received ROI packets for re-submission.

• SIN : Sub-image Identification Number (SIN) is used bythe sink, specially to reconstruct the undistinguishablesub-images (i.e., ROIs or Tiles having the same numberof packets and the same priority level).

Fig. 5. JPEG header format

Fig. 6. VSN header format

Remark: To best implement our scheme, we need an effec-tive management of data fragmentation at each VS node intomultiple packets/segments/fragments, in order to respect themaximum packet size or Maximum Transferable Unit (MTU)that a user can deliver to the network. One important feature ofsuch a block transfer is the use of NACKs messages discussedin the Section V-C [11]. Missing fragments from a large binaryobject is tolerable in case of image transfer over wireless link,which motivate us to tolerate some “losses” in ROIs packets.On the other end, reassembly of fragmented data is done atthe sink as soon as fragments arrived.

B. Semi-reliable scheme

The suggested scheme is implemented on each node and isdesigned for the case of coexistence of reliable and unreliabletraffic. It is inspired from class-based queuing model [10]. Twoqueues are used for both reliable (ROIs packets) and unreliable(tiles packets) traffic. The suggested scheme is illustrated in

Figure 7 and explained as follows. When a packet arrives at arelaying node, the Differentiator checks the field packet SI. IfSI = 0 (tile), we put the packet into the tile queue, otherwise(SI = 1) the packet is redirected to ROI queue. Just before itstransmission to the next hope, each packet in the ROI queuegoes through the Checker, which records the packet numberN. This is performed in order to notify the VS sender (bysending a NACK message) for a possible missed/lost packetsafter a fixed time, and forward the packet to the next hope.Tiles packets do not go through the Checker, because theyfollow an unreliable scheme, and are not acknowledged.

Fig. 7. Reliable scheme

An intermediate node decides to drop or to put an incomingpacket into its appropriate queue, according to the queuesize, the packet’s nature (ROI or tile) and the VS residualenergy. It starts discarding tiles packets if the queue is full,or if the residual energy is lower than the minimum energyrequired to process one image [5]. Since ROI packets follow areliable scheme, they are are free of drop, unless the networkis congested. Congestion problems are not considered in thiswork.

C. Reliability considerations

Reliability methods based on negative acknowledgement(NACK) are widely used in sensor networks in order to achievebetter energy conservation. Reliability in our context refers toensure the delivery of ROI packets towards the sink withoutany real-time considerations. When a relaying node detectspacket losses (by checking holes in the sequence number Nof received packets that have aged for too long) it requestsretransmission of missing packets by using NACK messages[11]. Even if NACK methods presents some drawbacks, it isused in our scheme for simplicity reason. NACK messages aresent in the reverse direction of the routing path establishedbetween the source and the node sending the NACK message,which necessitates some collaboration from the used routingprotocol discussed in the following Section. We Note that ourscheme can easily be integrated with the state of the art routingprotocol such as SPEED [12] or Directed Diffusion [13].

VI. NETWORK MODEL

A simplified multihop visual sensor network (Figure 8) canbe used to illustrate our proposal. Each node in the networkis assumed to consist of a visual sensor such as Cyclops

[14] connected to an embedded sensor such as Mica2 motes[15]. Each VS is responsible for capturing, processing andtransmitting relevant images from areas of interest to the sink.The following assumptions are used in our network model. AllVS nodes are suitably distributed in a strategic area to ensurethat the network is fully connected. Each node communicateswith its neighbours within its transmission range d. All nodesare immobile and energy constrained.

Fig. 8. A simplified routing scenario

The power consumption model for the radio is similar to theone proposed in [17]. The energy dissipated by a given VSfor sending a single bit is eTx = ee +eadα, and the consumedenergy in reception per bit is eRx = ee. Where ea is the energyconsumed by the transmit amplifier per bit over a distance of 1meter, ee is the energy dissipated by the transmitter electronicsper bit, α ∈ [2, 4] is the path loss exponent and d is the distancebetween the sender and the receiver [18]. Therefore, the totalenergy consumed for transmitting a bit between two nodes iseb = eTx +eRx. In our work, we consider the linear multihopscenario as in [17]. For n hops between the source and thesink, the multihop energy per bit can be stated as:

eb = neTx + (n − 1)eRx (6)

VII. SIMULATIONS

Several simulations are conducted using Matlab. First, weillustrate the suggested AIC model for the optimal selectionof ρ, which resolves the trade-off between the number oftransmitted coefficients and the received image quality. Afterthat, we show the gain in energy between the local methodand the global method.

We consider the network model as described in Section VI.The sensor network, formed by a hundred of nodes, is placedin a square region of size 50 × 50 m2. A node is considerednon-functional if its energy level El reaches 0, and cannottake an image if El < Ep (Eq. 4). The node communicationrange d is fixed at 10 m. The values of ea, ee and α of theradio transceiver model are ea = 100.10−12Joule/bit/m2,ee = 50.10−9Joule/bit and α = 2.5 [20]. The size of datapackets is 250 bytes.

To evaluate the processing energy consumed by JPEG andT-JPEG, we adopt the parameters of Mica2. From Equations4, 5 and 6, technical documentation [21] and some experiences[6], [22], we compute the total energy dissipated by JPEG andT-JPEG.

A. Illustration of AIC model

Three broad classes of grey level (8 bpp) images having dif-ferent resolutions were considered to illustrate the efficiency of

AIC model suggested in Section III for selecting ρo. The firstclass is related to the images containing much information,which can be classified as a high frequency images with highspatial details, for example Lena (Fig. 9.a). The second classrepresents the images that contain less information than thefirst class and can be classified as a high frequency imagewith low spatial content, for example Window (Fig. 9.b); andthe third class is closely related to those images containingfew information, i.e. low frequency with low spatial details,for example Beach (Fig. 9.c). These images have differentresolutions.

Fig. 9. Examples of image classes:(a) Class 1 (Lena); (b) Class 2 (Window); (c) Class 3 (Beach)

We perform several experimentations on these classes ofimages to illustrate the suggested AIC model, and henceselecting the appropriate ρo for each image class. A considerednumber of images (approximately 20 images per class) aresubject to AIC model described by Eq. 3 for selecting ρo.

As we have already noted, the value of ρ has two effects:1) an energy-effect which means that the energy consumed byone source node for processing an image increases with ρ; and2) a quality-effect which means that the received image qualityat the sink increases with ρ. The energy-effect is explainedby the fact that the number of DCT coefficients to be sentincreases with ρ, which necessitates more processing energy.The quality-effect is explained by the fact that the qualityof the received image decreases with D (Eq. 3) which itselfdecreases with ρ (and thus, the image quality increases withD). The optimal ρo that resolves the trade-off between the twoeffects is indeed the ρ that minimizes the AIC function (seeSection III).

Since images of the first class contain much spatial contrast,the optimal number of DCT coefficients to be sent will berelatively important compared to other classes. The secondclass of images contains less spatial contrast, and thereforeless DCT coefficients need to be sent. Whereas the imagesof the third class contain fewer spatial detail, and thus, fewerDCT coefficients need to be sent. The optimal ρo obtained forclasses 1, 2 and 3 are 6, 5 and 4, respectively.

To illustrate this, we consider the three images Lena,Window and Beach (Fig. 9) which are of Classes 1, 2 and3, respectively. Table I shows how the AIC function andthe received image-quality (determined by PSNR) evolveswith ρ for the three images. As we can see in Table I, thevalue ρo obtained for Lena, Window and Beach are 6, 5

and 4, respectively. The compressed images Lena, Windowand Beach using T-JPEG(6), T-JPEG(5) and T-JPEG(4), areillustrated in Figures 10, 11 and 12, respectively. An importantpoint is that we remark that the three images remain of goodquality, whereas we have saved energy.

Fig. 10. Compressed “Lena” using T-JPEG(ρo), (ρo = 6)

Fig. 11. Compressed “Window” using T-JPEG(ρo), (ρo = 5)

B. Comparing the performances of global and local methods

Let us study the energy-efficiency of the local method(Section III-B) in comparison to the global method (SectionIII-A). For illustration purpose, we consider the image ofLena. With the global method, we compute the unique optimalglobal ρo = 6 using Eq. 3. With the local method, Lena isdecomposed into one ROI R representing the face of Lenaand four tiles Ti(i = 1 · · · 4). The optimal value ρl

o of the

Class AIC and PSNR for ρ = 1 to 81 Lena ρ 1 2 3 4 5 6 7 8

AIC 4.011 3.5857 3.3448 3.2734 3.2420 3.2388 3.2390 3.2392PSNR 23.66 28.17 30.73 31.49 31.82 31.86 31.89 31.92

2 Window ρ 1 2 3 4 5 6 7 8AIC 3.20934 3.0456 2.8991 2.8026 2.7610 2.7623 2.7649 2.7679

PSNR 21.10 23.24 24.90 26.70 28.13 28.21 28.23 28.33 Beach ρ 1 2 3 4 5 6 7 8

AIC 2.3693 1.6169 1.5738 1.5660 1.5678 2.5700 1.5726 1.5756PSNR 24.83 27.94 31.43 33.25 34.03 34.05 34.08 34.11

TABLE IAIC AND PSNR OBTAINED FOR THE DIFFERENT ρ VALUES (1-8), FOR THE THREE CLASSES OF IMAGES.

Fig. 12. Compressed “Beach” using T-JPEG(ρo), (ρo = 4)

ROI R is computed by adapting Eq. 3 (replacing the imagesize N ×N by the ROI size K×L). While for each tile Ti weattribute a corresponding ρ which is necessarily smaller thanρo. For example, the four tiles may be assigned the values 1(Figure 4) or 0. These approaches, local and global, are thencompared with each other. With the local method, we can savemore energy while preserving a good image quality of the ROIat the sink, and thus we increase the lifetime of the network.We can also save more processing time and get a smallercompressed file size, in comparison with global method. TableII represents the obtained results. From this table, we note thatthe consumed energy with the local method is lower than theglobal method.

In the rest of this section, we simulate and study the effectof using the local method on the energy dissipated through thepath between the source and the sink, compared to the globalmethod. To facilitate the reader’s task, we use the followingnotations. If one image is partitioned into several sub-images(tiles and ROIs) to which are associated their correspondingρ’s, then the notation T-JPEG(ρ1, ρ2, ...) means that T-JPEG isapplied to these regions, where the ρ’s are ordered accordingto the positions of the corresponding regions, from left to rightand from top to bottom. For example, in the Lena of Figure9, where the tiles (ρ = 1) and the ROI (ρ = 6) are in the orderT1, T2, ROI, T3, T4, we write T-JPEG(1,1,6,1,1).

The processing and communication energies consumedthrough the path between the source and the sink are calculatedusing Eqs. 5 and 6, respectively. We suppose that the distancebetween the source and the sink takes several hops, from 1 to

30. In our simulations, we focus on unicast traffic pattern. Weconsider the same example of Lena image, and we computethe energy dissipated through the path used during a wholecycle1 to send this image using both local and global methods.We consider different numbers of hops between the source andthe sink. For a given number of hops, we use the same pathfor both methods, in order to evaluate clearly the energy gain.Our results are represented in Figure 13, which shows thatthe total energy dissipated when applying the local method inthe whole path is low compared to the energy dissipated withglobal method.

0 5 10 15 20 25 30

2

4

6

8

10

12

Number of hops between the source and the sink

Con

sum

ed e

nerg

y (m

j)

JPEGT−JPEG(6)T−JPEG(1,1,6,1,1)

Fig. 13. Total consumed energy (processing and communication) through thepath between the source and the sink for JPEG, T-JPEG and T-JPEG(1,1,6,1,1)

VIII. CONCLUSION

Two approaches (global and local) are proposed to tackle theenergy constraint problem when executing JPEG by a visualsensor. Both methods exploit the DCT energy compactionproperty by processing only a portion of each block of 8 × 8DCT coefficients. These methods are investigated to solve thetrade-off “minimize energy-maximize image quality”. Withthe global method, a unique optimal size is computed for allportions of DCT blocks of a whole image. Whereas in the localmethod, the original image is divided into ROIs and tiles. ROIs

1The cycle starts by image capturing and ends at the reception of the imageby the sink.

Global method Local method, four tiles and one ROI = Lena faceTiles - Tile 1 Tile 2 ROI Tile 3 Tile 4

ρ 5 1 1 5 1 1PSNR per sub-image - 25.67 21.75 31.61 24.86 22.75

PSNR per image 31.86 27.21Energy (mj) 5.87 1.90 0.87 0.51 0.5 0.42

Total energy = 4.2Processing time (s) 43.09 5.76 2.51 1.43 1.75 3.61

Total time = 15.066

TABLE IICOMPARING THE PERFORMANCES OF GLOBAL AND LOCAL METHODS

are sub-images for which a good image quality is required atthe sink, whereas tiles are sub-images for which a poor imagequality is tolerated at the sink. Each ROI is processed as awhole image for which a (local) optimal size is computedfor its portions of DCT blocks. While for tiles, the size isselected in respect to the user interest and the residual energyof a visual sensor. The optimal size (for both the global andlocal methods) is computed using an AIC model. Moreover,ROIs and tiles are sent differently, using a semi-reliablepriority queuing mechanism, which guarantees that ROIs aretransmitted before and more reliably than tiles. One directionof our future work is to introduce an automatic classifier whichrecognizes the image class and attributes automatically itscorresponding ρo. We plane also to implement our schemein a real test-bed, such as Mica2 motes.

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