A Fast and Efficient Topological Coding Algorithm for Compound Images
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A Fast and Efficient Topological Coding Algorithm for Compound Images Xin Li Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA Email: [email protected] Received 11 September 2002 and in revised form 8 June 2003 We present a fast and efficient coding algorithm for compound images. Unlike popular mixture raster content (MRC) based approaches, we propose to attack compound image coding problem from the perspective of modeling location uncertainty of image singularities. We suggest that a computationally simple two-class segmentation strategy is sufficient for the coding of compound images. We argue that jointly exploiting topological properties of image source in classification and coding stages is beneficial to the robustness of compound image coding systems. Experimental results have justified effectiveness and robustness of the proposed topological coding algorithm. Keywords and phrases: compound image coding, level set, location uncertainty, topological property, strongly connected component, rate-distortion optimization.
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INTRODUCTION
Compound image coding arises from various applications related to the storage and the distribution of document images. Document images usually contain the mixture of textual, graphical, and pictorial contents. Mixture raster content (MRC) model, a layered representation, has been widely used in the literature of compound image coding [1, 2, 3]. In spite of the popularity of MRC representation, the computational complexity of generating layers by image segmentation is prohibitive. For example, in MRC-based DjVu algorithm [2], segmentation stage often takes significantly longer time than the following coding stage. In this paper, we attack compound image coding from a different perspective. We argue that computationally expensive document segmentation [4, 5, 6] is not an indispensable component to compound image coding system. Instead, we propose a simple yet effective two-class segmentation strategy to accommodate the compound nature of document images. The key observation is that image coding does not need to fully separate images from texts, graphics, and pictures as document segmentation does. For the task of compression, we advocate that it is sufficient to separate the compound image into two subsources: texts/graphics for which location uncertainty of image singularities should be directly modeled in the spatial domain, and pictures for which wavelet representations have shown to be appropriate [7, 8, 9, 10]. It is easy to see that such two-class model can be viewed as a special case of MRC representation (i.e., lift texts from mask
layer to foreground layer). The advantage with our two-class model is reduced complexity. We will show that topological properties of two subsources provide a useful cue for fast segmentation. A lineartime algorithm based on finding strongly connected components [11] is proposed for the identification of textual/graphic regions. We then study how to exploit topological properties of image
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