An energy efficient image compression scheme for wireless multimedia sensor network using curve fitting technique
TL;DR: This work proposes an energy saving image compression technique for WMSN using curve fitting technique considering the application of post-disaster situation analysis through image capturing of the affected area and results confirm the scheme’s supremacy in WMSN application domain over existing methods.
Abstract: Wireless multimedia sensor network (WMSN) comprising of miniature sensor nodes is capable of processing multimedia data traffic such as still images and video from the environment. There is a wide range of applications which get benefited from such network. Unprocessed multimedia transmission is always expensive in terms of processing power, storage, and bandwidth. So, data processing is a challenge in WMSN. Exploring low-overhead data compression technique is a solution towards this problem. In this work we propose an energy saving image compression technique for WMSN using curve fitting technique considering the application of post-disaster situation analysis through image capturing of the affected area. Upon employing the method on the macroblocks of sensory image, curve fitting coefficients are generated and transmitted towards the sink thereby saves energy by transmitting reduced volume of data. Finally the design feasibility along with simulation results including statistical analysis is presented to evaluate efficacy of the scheme in terms of two conflicting parameters viz. energy consumption and peak signal to noise ratio. The comparative results confirm our scheme’s supremacy in WMSN application domain over existing methods.
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TL;DR: This paper designs a reinforcement learning based mechanism to perform QoS and energy balanced routing according to the knowledge of reliability and delay in WMSN, and shows that the energy consumption is reduced while ensuring QoS compared with the traditional cooperative protocol and the distributed adaptive cooperative routing protocol.
36 citations
TL;DR: A low-complexity energy efficient scheme to improve the transmission quality of images compressed by the embedded zerotrees of wavelet transforms and set partitioning in hierarchical trees algorithms in the WMSNs based on an orthogonal frequency-division multiplexing technique is proposed.
Abstract: Multimedia content delivery applications over wireless sensor networks have been progressively popular. The crucial obstacle to communicating compressed images over wireless multimedia sensor networks (WMSNs) has been the lack of suitable energy efficient processing architectures and strategies. In this letter, we propose a low-complexity energy efficient scheme to improve the transmission quality of images compressed by the embedded zerotrees of wavelet transforms and set partitioning in hierarchical trees algorithms in the WMSNs based on an orthogonal frequency-division multiplexing technique. The proposed scheme use a simple post-inverse discrete Fourier transform modified $\mu $ nonlinear transformation, called $\mu $ -MNLT. Simulation results show the effectiveness of the proposed approach, which gives an improvement of peak signal-to-noise ratio (SNR) of about 30 dB (at SNR = 8 dB) without any bit-error-ratio degradation.
24 citations
Cites background from "An energy efficient image compressi..."
...Unprocessed multimedia transmission (graphics, audio and video) is always expensive in terms of considerable storage capacity, processing power and transmission bandwidth [1], [2]....
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TL;DR: A new lossy compression approach using the Bayesian predictive coding (BPC), which is the same efficient as the linear predictive coding when handling independent signals which follow a stationary probability distribution and is more robust toward occasionally erroneous or missing sensor data.
Abstract: Wireless sensor networks (WSNs) generate a variety of continuous data streams. To reduce data storage and transmission cost, compression is recommended to be applied to the data streams from every single sensor node. Local compression falls into two categories: lossless and lossy. Lossy compression techniques are generally preferable for sensors in commercial nodes than the lossless ones as they provide a better compression ratio at a lower computational cost. However, the traditional approaches for data compression in WSNs are sensitive to sensor accuracy. They are less efficient when there are abnormal and faulty measurements or missing data. This paper proposes a new lossy compression approach using the Bayesian predictive coding (BPC). Instead of the original signals, predictive coding transmits the error terms which are calculated by subtracting the predicted signals from the actual signals to the receiving node. Its compression performance depends on the accuracy of the adopted prediction technique. BPC combines the Bayesian inference with the predictive coding. Prediction is made by the Bayesian inference instead of regression models as in traditional predictive coding. In this way, it can utilize prior information and provide inferences that are conditional on the data without reliance on asymptotic approximation. Experimental tests show that the BPC is the same efficient as the linear predictive coding when handling independent signals which follow a stationary probability distribution. More than that, the BPC is more robust toward occasionally erroneous or missing sensor data. The proposed approach is based on the physical knowledge of the phenomenon in applications. It can be considered as a complementary approach to the existing lossy compression family for WSNs.
11 citations
TL;DR: A new curve-fitting model has been proposed to be derived from the symmetric function (hyperbolic tangent) with only three coefficients, which will reduce the reconstruction error and improve fine details and texture of the reconstructed image.
Abstract: Image compression is one of the most interesting fields of image processing that is used to reduce image size. 2D curve-fitting is a method that converts the image data (pixel values) to a set of mathematical equations that are used to represent the image. These equations have a fixed form with a few coefficients estimated from the image which has been divided into several blocks. Since the number of coefficients is lower than the original block pixel size, it can be used as a tool for image compression. In this paper, a new curve-fitting model has been proposed to be derived from the symmetric function (hyperbolic tangent) with only three coefficients. The main disadvantages of previous approaches were the additional errors and degradation of edges of the reconstructed image, as well as the blocking effect. To overcome this deficiency, it is proposed that this symmetric hyperbolic tangent (tanh) function be used instead of the classical 1st- and 2nd-order curve-fitting functions which are asymmetric for reformulating the blocks of the image. Depending on the symmetric property of hyperbolic tangent function, this will reduce the reconstruction error and improve fine details and texture of the reconstructed image. The results of this work have been tested and compared with 1st-order curve-fitting, and standard image compression (JPEG) methods. The main advantages of the proposed approach are: strengthening the edges of the image, removing the blocking effect, improving the Structural SIMilarity (SSIM) index, and increasing the Peak Signal-to-Noise Ratio (PSNR) up to 20 dB. Simulation results show that the proposed method has a significant improvement on the objective and subjective quality of the reconstructed image.
8 citations
TL;DR: This paper proposes an energy saving video compression technique for WMSN by judicious combination of partial discrete cosine transform and compressed sensing that exploits the benefits of both the techniques towards fulfilling the objective of saving energy along with achieving desired peak signal to noise ratio (PSNR).
Abstract: Wireless multimedia sensor network (WMSN) is a special wireless sensor network (WSN) made up of several multimedia sensor nodes, specially designed to retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment. Due to strict inherent limitations in terms of processing power, storage and bandwidth, data processing is a challenge in such network. Further, energy is one of the scarcest resources in WSN, especially in WMSN and therefore, saving energy is of utmost importance. Data compression is one of the solutions of such a problem. This paper proposes an energy saving video compression technique for WMSN by judicious combination of partial discrete cosine transform and compressed sensing. This amalgamation exploits the benefits of both the techniques towards fulfilling the objective of saving energy along with achieving desired peak signal to noise ratio (PSNR). When the transform technique ensures low-overhead compression, compressed sensing guarantees the reconstruction of the same video with lesser amount of measurements. Performance of the scheme is measured both qualitatively and quantitatively. In qualitative analysis, overhead of the scheme is measured in terms of storage, computation, and communication overheads and the results are compared with a number of existing schemes including the base scheme. The results show considerable reduction of all such overheads thereby justifying the appropriateness of the proposed scheme for resource-constrained networks like WMSNs. In quantitative analysis, for both ideal and packet loss environment, the scheme is simulated in Cooja, the Contiki network simulator to make it readily implementable in real life mote e.g. MICAz. When compared with the existing state-of-the-art schemes, it performs better not only in terms of 34.31% energy saving but also in getting an acceptable PSNR of 35–37 dB and SSIM of 0.85–0.88 in ideal environment. In packet loss environment, these values are 32.9–35.5 dB and 0.81–0.85 respectively implying acceptable reconstruction even in packet loss environment. Further, it requires the least storage of 51.2 KB. The observation on simulation results is also justified by statistical analysis.
8 citations
References
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TL;DR: Existing solutions and open research issues at the application, transport, network, link, and physical layers of the communication protocol stack are investigated, along with possible cross-layer synergies and optimizations.
2,311 citations
TL;DR: The standard deviation is a valid measure of variability regardless of the distribution, and is used as an estimate of the variability of the population from which the sample was drawn.
Abstract: The terms “standard error” and “standard deviation” are often confused.1 The contrast between these two terms reflects the important distinction between data description and inference, one that all researchers should appreciate.
The standard deviation (often SD) is a measure of variability. When we calculate the standard deviation of a sample, we are using it as an estimate of the variability of the population from which the sample was drawn. For data with a normal distribution,2 about 95% of individuals will have values within 2 standard deviations of the mean, the other 5% being equally scattered above and below these limits. Contrary to popular misconception, the standard deviation is a valid measure of variability regardless of the distribution. About 95% of observations of any distribution usually fall within the 2 standard …
555 citations
TL;DR: This survey focuses on the video encoding at the video sensors and the real-time transport of the encoded video to a base station, and considers the mechanisms operating at the application, transport, network, and MAC layers.
Abstract: A wireless sensor network with multimedia capabilities typically consists of data sensor nodes, which sense, for instance, sound or motion, and video sensor nodes, which capture video of events of interest. In this survey, we focus on the video encoding at the video sensors and the real-time transport of the encoded video to a base station. Real-time video streams have stringent requirements for end-to-end delay and loss during network transport. In this survey, we categorize the requirements of multimedia traffic at each layer of the network protocol stack and further classify the mechanisms that have been proposed for multimedia streaming in wireless sensor networks at each layer of the stack. Specifically, we consider the mechanisms operating at the application, transport, network, and MAC layers. We also review existing cross-layer approaches and propose a few possible cross-layer solutions to optimize the performance of a given wireless sensor network for multimedia streaming applications.
407 citations
17 Oct 2008
TL;DR: Current research on prototypes of multimedia sensors and their integration into testbeds for experimental evaluation of algorithms and protocols for WMSNs are described and open research issues and future research directions are discussed.
Abstract: The availability of low-cost hardware is enabling the development of wireless multimedia sensor networks (WMSNs), i.e., networks of resource-constrained wireless devices that can retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment. In this paper, ongoing research on prototypes of multimedia sensors and their integration into testbeds for experimental evaluation of algorithms and protocols for WMSNs are described. Furthermore, open research issues and future research directions, both at the device level and at the testbed level, are discussed. This paper is intended to be a resource for researchers interested in advancing the state-of-the-art in experimental research on wireless multimedia sensor networks.
338 citations
TL;DR: The transmission of JPEG2000 images over wireless channels is examined using reorganization of the compressed images into error-resilient, product-coded streams which are shown to outperform other algorithms which were recently proposed for the wireless transmission of images.
Abstract: The transmission of JPEG2000 images over wireless channels is examined using reorganization of the compressed images into error-resilient, product-coded streams. The product-code consists of Turbo-codes and Reed-Solomon codes which are optimized using an iterative process. The generation of the stream to be transmitted is performed directly using compressed JPEG2000 streams. The resulting scheme is tested for the transmission of compressed JPEG2000 images over wireless channels and is shown to outperform other algorithms which were recently proposed for the wireless transmission of images.
258 citations