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Stereo images provide an enhanced sense of presence, and have been found to be operationally useful in tasks requiring remote manipulation or judgment of spatial relationships in contrast to ordinary image. A conventional stereo system with a single left-right pair needs twice the raw data as a monoscopic imaging system. As a result there have been increasing attention given to image compression methods. As an important part of the stereo pair coding, disparity estimation influences the precision and efficiency of the coding system. The traditional disparity estimation methods for stereo pair coding are mostly fixed-size block matching (FSBM). But the disparity vectors estimated by this method are not very accurate. In order to find more accurate disparity vector, adaptive-size block matching (ASBM) algorithm was used in some stereo matching algorithms. And this kind of algorithms selected an appropriate window based on the content of image that improves the verity of estimation. But the primary problem of it is computational complexity that prevents its applying in stereo coding. In this paper, a novel hybrid block matching (HBM) disparity estimation algorithm is proposed. And on the basis of it, a complete stereo coding scheme is introduced. In this scheme, conventional ASBM is improved and integrated with FSBM. Improved ASBM of this algorithm only uses the predicted error of the intensity to control the size of the matching window, which results in a reduction in complexity in contrast to traditional ASBM algorithms. We provide experimental results that show that our HBM achieves more accurate disparity vectors as compared to a simple FSBM and reduces the complexity of the traditional ASBM. Results also demonstrate that the proposed coding scheme provides higher mean peak signal-to-noise ratio (PSNR), about 0.7-1.2 dB, as compared with fix-size blockwise coding algorithm.
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