## 1 Introduction  
Traditional image coding techniques are developed for applications where the source is encoded once and the coded data is played back (decoded) many times. Hence, the encoder is very complex, while the decoder is relatively simple and cheap to implement. However, in many modern applications, such as camera arrays for surveillance, it is desirable to reduced the complexity and cost of the encoder.  
There are two enabling techniques, which allow reduced complexity encoders to be designed. The first one is distributed coding. Based on the information theoretic work by [PERSON] and [PERSON] and [PERSON] (1996), the coding allows correlated sources to be encoded independently, while the decoder takes advantage of the correlation between source data to improve the quality of the reconstructed images. It has been applied to both image (2,3) and video coding (4).  
The second technique is compressed sensing (CS) (5,6). It has attracted considerable research interest because it allows continuous signals to be sampled at rates much lower than the Nyquist rate for signals that are sparse in some (transform) domain. There have been some application of CS to image coding in the literature. A review is provided in Section 3. Earlier schemes apply CS to the whole image. More recently, block-based coding methods have been proposed (7, 8, 9, 10). The image is divided into non-overlapping blocks of pixels, and each image block is encoded independently. These methods generally use the same measurement rate for all image blocks, even though the compressibility and sparsity of the individual blocks can be quite different.  
Most proposed coding schemes use a block-based coding approach, where the image is divided into non-overlapping blocks of pixels and each image block is coded independently. They generally use the same measurement rate for all image blocks, even though the compressibility and sparsity of the individual blocks can be quite different (7, 8, 9, 10). For effective and simple CS-based image coding, it is desirable to use a block-based strategy that exploits the statistical structure of the image data. In (11,12), the measurement rate of each block is adapted to the block sparsity. However, it is not easy to estimate the sparsity or compressibility of an image block from the CS measurements alone.  
In this paper, we take the distributed approach to CS block-based image coding. The image is divided into non-overlapping blocks, and CS measurements for each block are then acquired. Some blocks are designated as key blocks and others as non-key blocks. The non-key blocks are encoded at reduced rates. At the decoder, the reconstruction quality of the non-key blocks is improved by using side information generated from the key blocks. This side information is purely in the CS domain and does not require any prior decoding, making it very simple to generate. We also consider key issues, including the choice of sensing matrix, the measurement (sampling) rate and the reconstruction algorithm.  
The rest of this paper is organized as follows. Section 2 provides a brief overview of the theory of CS. A review of related works in CS-based image compression is presented in Section 3. Our distributed image coding scheme is presented in Section 4. Section 5 presents experimental results to show the effectiveness of this image codec.