Image Transmission with Data Recovery Techniques

Wireless Multimedia Sensor Networks (WMSN) have an increasing variety of multimedia based applications including image and video transmission. In these types of applications, multimedia sensor nodes aim to provide both perceptual quality at the end-node and energy efficiency at the intermediate nodes. Hence, robust image and video communication in an energy efficient manner have become more imperative due to the ubiquitous proliferation of multimedia applications over wireless sensor networks.

Various image transmission problems have been studied in Wireless Sensor Networks [28, 29, 30, 31, 32, 12]. Due to the strict energy constraints of the sensor nodes, the majority of the studies focus on energy efficient image transmission in different aspects involving preprocessing of the overlapped images [33, 34] and compression of the transmitted image [29, 31, 35, 36, 37]. Few studies have proposed to increase the performance of the image transmission in terms of perceptual quality and the other application layer requirements in WMSN [38, 39, 40]. These studies have utilized well known error correction techniques such as Automatic Repeat Request (ARQ) [41] and Forward error correction (FEC) codes [42] in order to mitigate the wireless transmission errors. In this context, Error Concealment (EC) is another error mitigating technique, which is based on several coding standards such as discrete wavelet transform (DWT) or discrete cosine transform (DCT) to obtain the subbands of the image [43]. EC reconstructs the distorted multimedia data as closely as the original one without increasing the bandwidth demand as well as avoiding the burden of retransmissions and consequent delay [17]. It is performed on the received data based on spatial, spectral or temporal redundancies. [16, 44]. However, EC suffers from energy and computational overhead both at the source and at the sink. In [45] and [16] different approaches have been proposed for image and video EC considering the tradeoffs between various design forces. In spite of that, these techniques are proposed for the applications with certain channel and source characteristics, which are not suitable for WSN environment.

ARQ techniques are based on the detection and retransmission of erroneous packets. As ARQ schemes require the retransmission of the packets, they may significantly increase the transmission delay, which may not be acceptable for multimedia applications. [38] has developed a cross-layer design which integrates adaptive modulation and coding at the physical layer with a truncated ARQ protocol at the data link layer, so as to increase spectral efficiency (bit rate per unit bandwidth) under specified delay and error performance constraints. The results show that retransmissions mitigate stringent error-control requirements on modulation and coding and present significant spectral efficiency performance. They also suggest that making an adjustment between retransmission and spectral efficiency enables a desirable delay-throughput tradeoff in practice.

Forward error correction schemes encode the transmitted data [42] by using additional information called error correction codes. These codes allow data to be recovered at the receiver upon error detection. Although FEC schemes recover a certain number of errors in the packet, they may require a significant increase in the transmission bandwidth, which may be prohibitive for applications that run over links with low-bandwidth, bad channel conditions, etc. Depending on the channel conditions and networks resources, a FEC/ARQ hybrid scheme may be used for applications [46, 47, 48].

[40] proposes a novel scheme for image transmission in wireless sensor networks. Firstly, multiple bit streams of the compressed image are produced by using discrete wavelet transform. In order to achieve energy efficiency in the transmission, energy cost caused by both control overhead and switching is decreased by using bursty small fragments during communication. FEC channel coding (RCPC/CRC) and ARQ is used to combat the channel errors. Unequal error protection strategy is also implemented with the RCPC/CRC method. The results show that if bit error rate is below a certain value, FEC coding is more energy efficient than ARQ schemes. Otherwise, FEC codes can not correct the erroneous data due to their limited correction capability.

Wu et.al[39] have studied a reliable transmission scheme that utilizes FEC coding and investigates the trade-off between energy and transmitted image quality. The transmission scheme is based on multipath transmission and error correction with FEC coding so as to achieve reliable image transmission. In the proposed model, two copies

of the packets are transmitted over overlapped multipaths which converge to more powerful nodes called cluster heads (CH). At the CH, the replica of the data are fused in order to decrease the transmission errors in the packets. Then, the CH retransmits the corrected packets towards the sink via other CHs. Their results indicate that their proposed scheme improves the perceptual quality in terms of PSNR at the sink.

In [49], an energy efficient image transmission scheme based on DWT and semi-reliable transmission is proposed to provide a reliable image transmission. Firstly, DWT is applied to the original image to produce four sub-bands (Low-Low, Low-High, High-Low, High-High) of multi resolution. Then, each sub-band is decompressed by using entropy coding for lossless compression due to its low computational and less complex nature. A prioritization operation is performed on the packets to be transmitted depending on their resolution level. Consequently, multiple packets with different priorities are transmitted. In this transmission scheme, as high priority packets are transmitted reliably, low priority packets are transmitted in a semi-reliable manner which utilizes priority based packet dropping mechanism to save energy.

WMSN applications require QoS based image transmission to satisfy the end-user in terms of perceptual quality, timeliness and etc. Hence, some essential factors identifying image characteristics (intra-image prioritization, etc) and vital interactions between application layer QoS requirements and underlying network conditions should be taken into account. However, the mentioned studies focus on the energy issue without considering the image quality requirement perceived by the end-user. Furthermore, the studies, investigating the performance of the image transmission in terms of packet loss and perceptual quality, do not consider image characteristics and physical layer communication parameters in their solutions.