Distributed Image compression in wireless sensor networks using intelligent methods

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1 International Research Journal of Applied and Basic Sciences 2013 Available online at ISSN X / Vol, 4 (7): Science Explorer Publications Distributed Image compression in wireless sensor networks using intelligent methods Mahaasa Dabestani *1, Mohammad-Shahram Moin 1 1. Department of Electrical and Computer Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran *Corresponding Author M.dabestani@qiau.ac.ir ABSTRACT: The availability of low cost hardware like microphone and CMOS cameras has led to spread in multimedia wireless sensor network. Data transfer is considered one of the high cost tasks in wireless sensor network consuming more than 80 percent of dedicated energy. The most appropriate way to reduce energy consumption is to use an appropriate compression image technique. Designing a compression method requires balance and careful estimation among compression ratio factors, quality, complexity, and time. The present article presents a new method, unlike previous ones, focusing only on the structure of the network. In addition, it tries to deal with low compression rate along with high quality of recovered images and reduction in energy consumption. Hence, the method used here is Distributed Vector Quantization (DVQ) whose codebook, being real-time and dynamic, was created by applying neural network of adaptive resonance theory. The results from tests on primary images demonstrate the accuracy and efficacy of proposed method. Keywords: Image compression, Distributed, Wireless sensor networks, Vector quantization, Adaptive resonance theory INTRODUCTION Due to an increase in computer and internet technology, multimedia input like images, videos, audios etc. are used more than before. Recent developments in MEMS and NEMS technologies as well as production of cameras like CMOS, small-sized microphones with low energy consumption that can get the available multimedia contents from the environment, multimedia wireless sensor network has progressed. This along with quick hardware improvement and size compression enables single sensor set to be equipped with modules to collect audio and video data. Numerous functions have been devised for this kind of network most of which are often applied to military, defense and controlling systems (Yick et al., 2008; Akyildiz et al, 2007). The nodes encounter resource limits like energy, memory, transfer bandwidth and processing potential. Since image processing is impossible, the images have to be transferred. Data transfer is one of the high cost tasks in wireless sensor network due to node sensor limits. Therefore, by making use of compression techniques, it is possible to decrease energy consumption by reducing the number of transferred bits. Image compression is applying special methods and techniques to reduce the volume of transferred images so that it can occupy less space. Methods used in compressing images of sensor network are suitable and reduce the volume of transferred data. There is a balance between energy use, memory and bandwidth so that the more compression rate, the less energy consumption and bandwidth. As nodes in sensor network encounter energy limit, additional compression of data can lead to extra energy use and memory. It is worth mentioning that data sending and receiving cost in these networks is much more than doing computations in nodes. Distributed processing in sensor network can be a wonderful finding (Sirsooksai et al, 2012; kimura et al.,2005). Most of the available compression algorithms are not suitable for sensor network because of their computational complexities that cause delays in network, increase memory traffic and energy consumption. The yardstick of choosing a method determines that selected algorithm should have most of the major features of sensor networks that include the quickest and most complete potential to process an image, low memory requirement, high compression quality, low system complexity, and low computational load. By image compression in this paper we mean dynamic distributed image compression in wireless sensor networks by making use of intelligent methods. The approach dealt with in it functions to give a new method of

2 distributed image compression. The reason for the application of this kind of neural network is its dynamic and realtime features making it appropriate for applications like this. ART neural networks have not only the potentiality for quick learning but also are dynamic and self-organizing. Distributed compression method was used in this research in order to reduce energy consumption and increase network longevity. This kind of distribution is in accordance with vector quantization compression that has easy and proper implementation. Another important factor for this choice is the quality of reconstructed images and required time to transfer data from its source to a new device. Unlike previous methods, the present method will consider the quality of reconstructed images and network lifetime simultaneously. Conducted experiments show that implementation of this method leads to increase in compression rate and decrease in sent bits per pixel. Results from simulations indicate this method, in addition to bit reduction, decreases the amount of occupied memory for nodes and energy consumption. The remainder of the paper is organized as follow. In section 2, we briefly review related work. Section 3 provides a detailed description of the proposed algorithm. Simulation of proposed compression algorithm and implementation scheme are presented in section 4. Conclusion is given in section 5. Related work Energy consumption is a critical problem because affect the lifetime of WMSNs other problem is bandwidth constraint. Many methods have been proposed to solve this Problem such as compression or routing protocols (Anatasi, 2009). Researches on WMSN image compression can be divided into two categories: one using the WMSN architecture attribute in image compression and the other which didn't notice this properties of WMSN. Jamali et al.(2010) in proposed a DSC coding for compression. This algorithm relies correlation between the sensor nodes. Chih-chung et al. (2010) in proposed a new WSN architecture using JPEG-LS compression. Every received data suppose like raw data of pixel and these raw data are group and collect until make an image. Then compress with Improved JPEG-LS. This mechanism has good compression ratio. Nasri et al.(2010) in proposed new scheme that reduce required memory. this scheme use JPEG2000. Pu Wang (2010) in proposed an Entropy based divergence measure (EDM). this scheme can reduce 10%-23% total coding rate. Qin Lu et al.(2009,2008) in proposed a LBT based image compression. In this scheme every node do part of this LBT based algorithm so it reduces the hardware cost of every node so prolongs the lifetime of wireless sensor network. As see in above works all of this paper notice more in one of factors that important in Image compression in WMSN this factors are energy consumption, compression ratio and quality of reconstructed image, delay. In this paper we proposed new scheme which notice all of this factors. We use idea of Qin Lu scheme which divide algorithm process and every of divided process execute in one node. In this paper a new scheme based on Vector Quantization Compression (Wern Chew et al, 2008). It has three phase: codebook generation phase, encoding phase and decoding phase. PROPOSED METHOD A number of images were taken from the environment. Then, using nodes, the algorithms of image compression were implemented in a distributed manner to send the data to the target device. By doing so, energy consumption decreases significantly and network lifetime increases. According to vector quantization method, the proposed approach includes three phases: codebook generation, coding, and decoding. What follows is a description of each phase and its related standards. Furthermore, the structure of sensor networks, discussed in this paper, was so designed that a camera node is placed in a permanent place to capture images from the environment and some scalar nodes placed in the environment do just the processing and data transfer. Fig 1 shows a model of this structure. Vector Quantization technique is one of the lossely compression techniques that is highly used nowadays (Tsai et al, 2009; Vlajic et al, 2004; Lai et al,2008). The main reasons are its simplicity of implementation and acceptable quality of images. This feature enables the operators to use it in distributed form in a sensor network. Vector quantization consists of codebook generation, coding, and decoding phases. The key factor to have vector quantization is to have a proper codebook (Tsai et al, 2009;Lai et al,2008). Almost all algorithms used to generate codebooks, due to repetitive computations, have low speed. Consequently, they need an initial vector whose amount influences the results significantly. To solve the problem, ART neural network, which is dynamic and has high speed, was used. As a result, a high quality codebook is produced and the reconstructed images taken from that are of high quality.

3 Codebook generation phase Unlike previous methods, the present approach codebook not only needs no initial but also it can do the job dynamically and without repetitive computations. It, unlike Vlajic et al (2004), uses this vector as a final codebook. it use fast Learning. So a series of pre-processing is needed in advance. The main point to be mentioned here is proper parameters. That is, appropriate parameters of quick learning mode which are totally different from those of slow learning mode should be picked up to work within adaptive resonance theory. It helps create high quality images. The less time a codebook creation takes, the better. In this step, first we choose an image as an input and do necessary pre-processing on it. Then, we apply CGART2 algorithm to create a codebook. This algorithm is shown in Fig. 1. The created codebook is sent to a receiver via sink to perform coding operation to adjacent nodes of the camera and decoding. CGART2 algorithm operates in a way that a pre-processing similar to binary formation by BTC method (Delp, 1979) is applied on input vectors. By doing so, we intend to use ART2 quick learning mode. Since quick learning is appropriate for second type of continuous data, that is, noisy binary, a series of pre-processing is necessary to be done on the data (Fausett, 1994). Encoding Phase Coding phase succeeds codebook generation phase through which the first image was received. In this step, after images have been captured from the environment around the camera node, they are sent to several neighboring nodes in block form to be coded. By receiving the blocks, each of these nodes extract indexes of the blocks from codebook table. It is so that any index whose codeword has the nearest amount to the selected block is chosen. The nearest block is determined by Euclidean distance so that the shorter the distance, the better. After finding specific indexes, the nodes code them by Huffman coding system. This kind of compression or coding is called distributed coding. To improve reconstructed image quality, it is possible to use blocks' mean and standard deviation, the results come in section 4. Further explanation of this will be given in the simulation results section. Any pack is divided into the number of nodes neighboring the image. If n stands for the number of nodes and bs for block size, the total block each node receives for coding (ncb) equals: After receiving an image, each node compares the blocks to available codeword and picks the closest word's index. The code word, as mentioned before, is determined according to Euclidean distance. On the other hand, since distributed operation takes less time than undistributed mode, that is, time spent in distributed mode equals 1/n that of undistributed form, the compression process will take place through less delay and high speed. Decoding phase This phase is carried out by the receiver. First binary bit range is decoded and its equivalent index is determined using Huffman decoding system. Second by making use of the codebook that had been sent to the receiver, the block related to the index is derived. The image is reconstructed by putting related blocks together. As it was mentioned in the coding phase, the mean and standard deviation of blocks are used to increase the quality of the reconstructed image. Results drawn from all of the phases will be demonstrated in the simulation and results sections. Simulation results Lenna was used to do the simulation. codebook sizes were often from 32 to.we use Matlab R2010a to do our simulation. Every step of an algorithm needs special standard study of its own. According to algorithm steps, results are taken and standards are analyzed. In the first step, as mentioned before, a codebook should be derived for use in later steps. Time is the primary criterion in this step. The less the time spent for codebook production, the better. Table 1 shows required time for three images with different codebook sizes. Table 2 shows proposed approach in this paper compared with other present approaches. The main criteria in the second phase are the required time for indexing, energy, compression rate, and the quality of reconstructed images. Results for images in a non-mean mode are shown in Fig.2. As the figure shows, the image includes all edge details while all blocks contain the same average of gray levels.

4 Figure 1. model of WMSNs structure in this paper Table1. CGART2 execution tim (in second)for three images with different codebook sizes Lena512 Baboon512 Peppers512 Average Time Table 2. Comparison of computing time (in second) for various fast codebook generation algorithm with different codebook sizes m GLA LC FVQ NPS PRELC PREGLA PREFVQ PRENPS CD PRECD CGART Table 3. PSNR and Bit rate for reconstructed image with every blocks mean lenna PSNR(db) Bit rate(bpp) Figure 2. Recunstructed Lenna

5 To solve this problem, gray level average of each block is used to reconstruct the image. Table 3 shows the amounts relevant to quality and rate of compression for different codebook sizes (block size: 2 2). In addition energy consumption computing comes in Table.4. In this computation as (Karl et al,2005), we consider every computation cost for every bit is 8nj and sending cost for every bit is 1j. Also consider Energy comes in form of (nj /block) and we have block in image. According to Table 3, compression rate and quality for the codebook is in accord with 64 size. As the codebook size is small, so its transfer cost decreases during codebook generation. Table 4. Energy consumption per pixel of the image in compression algorithm(using average) Task Euclidean( in encoding phase) mean (for every block) Energy consumption (nj/block) However as we see in Figure 3, images have low quality. To solve the problem, in addition to mean, standard deviation is used to reconstruct them. Figure 4 and Table 5 show the image after applying changes. 704 Figure 3. Recunstructed Lenna Figure 4. Reconstructed image using mean and standard deviation (block size= 4 4) Table 5. PSNR and Bit rate for reconstructed image with every blocks mean standard (4*4) blocks PSNR(db) Bit rate(bpp) Lena

6 Consumption of energy per block of images using average and standard deviation is shown in Table.6. In this condition we have blocks in image. Table 6. Energy consumption per pixel of the image in compression algorithm(using average and standard deviation, block size= 4 4) Task Euclidean( in encoding phase) mean (for every block) standard deviation (for every block) Energy consumption (nj/block) As see if we use mean and standard deviation we improve quality of reconstructed image and increase compression ratio (bpp) but we use more energy. To solve this problem we use 8 8 blocks instead of 4 4 ones (Table.7). In this condition we have 4096 block in image. Table 7. Energy consumption per pixel of the image in compression algorithm(using average and standard deviation, block size= 8 8) Task Euclidean( in encoding phase) mean for every block standard deviation for every block Energy consumption (nj/block) Table 8.PSNR and Bpp for recounstrucetd image with every blocks mean standard deviation, block size= 8 8 (8*8) blocks Lena PSNR bpp Figure 5.Reconstructed image using mean and standard deviation (block size= 8 8) In this case, energy use is lower than the two former amounts. Moreover, compression rate and encoding time decreases. Although the quality of the reconstructed images gets a bit lower than that in the previous mode, it is not considered a disadvantage because of low energy consumption and high compression rate. Results from this mode are shown in table 8 and figure 5. Table 9. Comparison of proposed method with Qin Lu et al method [10],[11] Lenna ( ) PSNR(db) Bit rate (bpp) energy consumption for total image in compression algorithm(j) mean (block size= 2 2) mean and standard deviation block size= 4 4)( mean and standard Deviation (block size= 8 8) [10], [11]

7 Analysis Table.9 shown result of this way improve Energy consumption, encoding time,bit rate, Memory use, Encoding time and of course our proposed method save the quality of reconstructed image in comparison with Qin Lu et al. algorithm. CONCLUSION Simulation test results indicate that codebook generation phase which, is an adaptive resonance theory and dynamic for codebook generation, saves time 50% more than previous methods. The second phase is coding operation that is carried out in distributed form among neighboring nodes. It saves time from 1 to 1/n depending on the number of neighboring nodes. The quality of reconstructed images as well as compression rate was improved in this phase. Furthermore, unlike previous methods related to WMSN compression, PSNR, Bit rate, time consumption, used memory, and energy consumption decrease were taken into consideration simultaneously. REFERENCES Akyildiz IF, Melodia T, Chowdhury KR A Survey on Wireless Multimedia Sensor Networks. Computer Networks. 51: Anatasi G, Conti M, Francesco MD, Passarella M Energy Conservation in Wireless Sensor Networks: A Survey. Ad Hoc Networks. 7: Chew Li W, Li-Minn A, Seng Kah P Survey of Image Compression Algorithms in Wireless Sensor Networks. Paper presented at the 4th International Symposium on Information Technology, Kuala Lumpur,Malaysia,26-28 Aug Delp E, Mitchell O Image Compression Using Block Truncation Coding. IEEE Trans. Communications. 27: Fausett L Fundamentals of Neural Networks. Prentice Hall. Jamali M, Zokaei S, Rabiee HR A New Approach for Distributed Image Coding in Wireless Sensor Networks. Paper presented in 2010 IEEE symposium on ISCC, Riccione Italy June Karl H, Willing A Protocols and Architectur for Wirless Sensor Networks. Wiley. kimura N, Latifi S A Survey on Data Compression in Wireless Sensor Networks. Information Technology: Coding and Computing (ITCC'05). 2:8-13. Lai J, Liaw Y, Liu J A Fast VQ Codebook Generation Algorithm usingcodeword displacement. Pattern Recognition. 41: Lin C, Chuang C, Chiang C, Chang R A Novel Data Compression Using Improved JPEG-LS in Wireless Sensor Networks. Paper presented in the 12th international Conference on ICACT, Phoenix Park, 7-10 Feb Lu Q, Luo W, Wang J, Chen B Low-Complexity and Energy Efficent Image Compression Scheme for Wireless Sensor Networks. Computer Networks. 52: Lu Q, Luo W, Ye X Collaborative In-Networks Processing of LT Based Image Compression Algorithm in WMSNs. Paper presented at 1st International Workshop on ETCS. Wuhan, Hubei, 7-8 March Nasri M, Sghaier H, Maaref H Adaptive Image Transfer for Wireless Sensor Networks. paper presented in 5th Intenational conference on DTIS. Hammammet, March Sirsooksai T, Keamarungsi K, Lamsrichan P, Araki K Practical data Compression in Wireless Sensor Networks: A Survey. Journal of Networks and Computer Applications. 35: Tsai C, Lee C, Chiang M, Yang C A Fast VQ Codebook Generation Algorithm Via Pattern Reduction. Pattern Recognition Letters. 30: Vlajic N, Card HC Vector Quantization of Images Using Modified Adaptive Resonance Algortihm for Hierarchical Clustering. IEEE Trans. Neural Networks. 12: Wang P, Dai R, Akyildiz IF Collaborative Data Compression Using Clustered Source Coding for Wireless Multimedia Sensor Networks. paper presented in INFOCOM. san Diego, CA, March Yick J, Mukherjee B, Ghosal D Wireless Sensor Network Survey. Computer Networks. 52: