The increasing demand for 3D imaging and recent developments of autostereoscopic displays will accelerate the usage of 3D systems in various areas. However, limited channel bandwidth is, as for monocular images, the main bottleneck for realizing 3D systems. As a result, an efficient compression algorithm will be essential to reduce the bandwidth requirement while maintaining the perceptual visual quality at the decoder. In this paper, we will focus on compression of stereo images. When it comes to stereo image coding, we can take advantage of binocular redundance by using disparity compensation. The most popular disparity compensation method approaches so far have ben block based methods, due mostly to their simplicity. Block based methods, however, may suffer from blocking artifacts at low bit rates due to the uniform disparity assumption within a fixed block. Meanwhile, if we reduce the block size, the disparity estimation may suffer from various noise effects which result in increases of bit rates for the disparity. Considering these observations, we estimate disparity based on a small block or a pixel with thee energy equation derived from the MRF model. In order to prevent oversmoothing across boundaries, we use the combined intensity edges of two images as an initial disparity boundary. Then, we segment the resulting smooth disparity field. Finally, the disparity and the starting position are encoded using DPCM and the corresponding boundary is encoded using Run Length Chain coding. At the end of this paper, we present experimental results.