In this paper, we propose a novel object driven, block based algorithm for the compression of stereo image pairs. The
algorithm effectively combines the simplicity and adaptability of the existing block based stereo image compression
techniques [1-6] with an edge/contour based object extraction technique to determine appropriate compression strategy for
various areas of the right image. Extensive experiments carried out support that significant improvements of up to 20% in
compression ratio can be achieved by the proposed algorithm, compared with the existing stereo image compression
techniques. Yet the reconstructed image quality is maintained at an equivalent level in terms of PSNR values. In terms of
visual quality, the right image reconstructed by the proposed algorithm does not incur any noticeable effect compared with
the outputs of the best algorithms.
The proposed algorithm performs object extraction and matching between the reconstructed left frame and the original right
frame to identify those objects that match but are displaced by varying amounts due to binocular parallax. Different coding
strategies are then applied separately to internal areas and the bounding areas for each identified object. Based on the mean
squared matching error of the internal blocks and a selected threshold, a decision is made whether or not to encode the
predictive errors inside these objects. The output bit stream includes entropy coding of object disparity, block disparity and
possibly some errors, which fail to meet the threshold requirement in the proposed algorith
