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Compression of line-drawing images using vectorizing and feature-based filtering

A three-stage method for compressing bi-level line-drawing images is proposed. In the first stage, the raster image is vectorized using a combination of skeletonizing and line tracing algorithm. A feature image is then reconstructed from the extracted vector elements. In the second stage, the original image is processed by a feature-based filter for removing noise near the borders of the extracted line elements. This improves the image quality and results in more compressible raster image. In the final stage, the filtered raster image is compressed using the baseline JBIG algorithm | Compression of line-drawing images using vectorizing and feature-based filtering Pasi Franti Eugene I. Ageenko Department of Computer Science University of Joensuu P.O. Box 111 Fin-80101 Joensuu FINLAND Alexander Kolesnikov Igor O. Chalenko Institute of Automation and Electrometry Russian Academy of Sciences Pr-t Ak. Koptyuga 1 Novosibirsk-90 630090 RUSSIA Abstract A three-stage method for compressing bi-level line-drawing images is proposed. In the first stage the raster image is vectorized using a combination of skeletonizing and line tracing algorithm. A feature image is then reconstructed from the extracted vector elements. In the second stage the original image is processed by a feature-based filter for removing noise near the borders of the extracted line elements. This improves the image quality and results in more compressible raster image. In the final stage the filtered raster image is compressed using the baseline JBIG algorithm. For a set of test images the method achieves a compression ratio of 40 1 in comparison to 32 1 of the baseline JBIG. Keywords image compression JBIG raster-to-vector conversion context modeling feature based filtering. 1. INTRODUCTION Lossless compression of bi-level images has been well studied in the literature and several standards already exist 1 . In the baseline JBIG the image is coded pixel by pixel in scan raster order using context-based probability model and arithmetic coding 2 . The combination of already coded neighboring pixels defines the context. In each context the probability distribution of the black and white pixels are adaptively determined. The current pixel is then coded by QM-coder 3 the binary arithmetic coder adopted in JBIG. The baseline JBIG achieves compression ratios from 10 to 50 for typical A4-size images. The pixelwise dependencies are well utilized and there is no much room for improvement. Remarkable improvement has been achieved only by specializing to some known image types and exploiting .