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Top1. Introduction
Widespread multimedia devices and smart applications are increasing the amount of visual data transmitted through wireless channels. These aspects have created demand for efficient transmission of multimedia data and the transmission of images is an important application. An important metric for performance of these applications is the quality of the data received by the user. MIMO systems made high data rates realizable along with ensuring reliability all by exploiting the spatial diversity (David, 2005). To reduce the requirements for bandwidth, compression schemes are adopted while ensuring reconstruction quality. However, a compression scheme in general and for JPEG in particular is a lossy technique and vulnerable to transmission errors (Song, 2002).
Despite the fact that most of the image compression standards and especially JPEG is lossy, it is also important to know that these compression algorithms transform an image block to equivalent block of bit streams with different importance of individual data within those streams. In this paper, JPEG standard is considered which uses discrete cosine transform (DCT) as main operation of data compression (Pennebaker, 1993). DCT transforms pixel values of an image block to respective frequency coefficients. An example of an 8 x 8 image block is shown as matrix M and it’s respective DCT is also shown as matrix N. Although the dimensions of DCT block are same as image block but data members of DCT matrix have different level of importance in image reconstruction. The first entry in matrix N, is the dc value and values nearby are low frequency components, up to last members of matrix constituting highest frequencies. DC value and low frequency components are most important for decoding an image. If these could be received error free, image can be decoded to an acceptable level. This property attracts researchers to design transmission schemes which are source aware to get better results. In past few years, image transmission research is focused on methods that take in to account compression matrix statistics.
Based on above discussion this part looks on strategies adopted to take advantage of compression transformation matrix. One popular method is unequal error protection (UEP) which can be realized using many different techniques. The idea behind UEP is driven by the fact that different portions of bit stream have different impact on the quality of the decoded image. The UEP scheme exploits the hierarchical structure of the coded bit stream and assigns higher protection to the more important parts. Several UEP techniques have been proposed in the literature for efficient transmission of bit streams over wireless channels, which are based on assigning variable forward error correction (FEC) coding to different portions of the bit stream according to their importance (Said, 1996).
Some methods are commonly known as joint source-channel coding (JSCC), and joint source coding and transmission power allocation depending on the type of joint design. The main idea behind these joint design techniques is to allocate the available resources in such a way that more important data suffer less distortion at the cost of more distortion for less important data, with the goal of minimizing overall distortion in the received images. These resources can be source and channel coding bits, total transmission power, delay, etc. By using such joint design techniques, significant quality gains can be achieved without violating constraints on different available resources. JSCC is the most commonly studied joint design problem for image communication in the literature. Another important joint design problem is that of transmission power allocation and optimization for image communication. The main goal for such problems is either to minimize the total distortion with a constraint on available transmission power, or to minimize the power usage with a constraint on maximum tolerable distortion (Eisenberg, 2002).