Segmentation using Codebook Index Statistics for Vector Quantized Images

In this paper, the segmentation using codebook index statistics (SUCIS) method is proposed for vector-quantized images. Three different codebooks are constructed according to the statistical characteristics (mean, variance, and gradient) of the codewords. Then they are employed to generate three different index images, which can be used to analyze the image contents including the homogeneous, edge, and texture blocks. An adaptive thresholding method is proposed to assign all image blocks in the compressed image to several disjoint regions with different characteristics. In order to make the segmentation result more accurate, two post-processing methods: the region merging and boundary smoothing schemes, are proposed. Finally, the pixel-wise segmentation result can be obtained by partitioning the image blocks at the single-pixel level. Experimental results demonstrate the effectiveness of the proposed SUCIS method on image segmentation, especially for the applications on object extraction.