Showing papers by "Wen-Hsiang Tsai published in 2000"

Spatial-domain image hiding using image differencing

Abstract: A method to embed a secret image into a cover image is proposed. The method is based on the similarity among the grey values of consecutive image pixels as well as the human visual system's variation insensitivity from smooth to contrastive. A stego-image is produced by replacing the grey values of a differencing result obtained from the cover image with those of a differencing result obtained from the secret image. The process preserves the secret image with no loss and produces the stego-image with low degradation. Moreover, a pseudorandom mechanism is used to achieve cryptography. It is found from experiment that the peak values of signal-to-noise ratios of the method are high and that the resulting stego-images are imperceptible. Even when the size of the secret image is about a half of the cover image.

Document image segmentation and quality improvement by moiré pattern analysis

Abstract: Moire patterns are distortions on the results of scanning printed documents. However, the patterns can be utilized in document image segmentation and quality improvement. The moire phenomenon comes from sampling periodical structures in images, such as halftone screens, color components, and text galleys which often appear in printed magazines and newspapers. The generated moire patterns appear in the scanning result in the form of obvious periodical patterns, color skew, and color noise on the edges of artworks. The moire pattern degrades the scanning result and makes document analysis more difficult. A new approach to document image segmentation and quality improvement by moire pattern analysis is proposed. A scanning resolution, called the conductor of screen sharing, is proposed to control the moire pattern. With the resolution, moire patterns are generated and enhanced in certain designed areas in the frequency domain. Then, a logical filter, called the comb filter, is proposed to detect the moire pattern. The new method, which is based on the sampling theory and moire analysis in the frequency domain, is actually performed in the spatial domain by re-sampling and logical filtering. The proposed method can efficiently extract gray or color pictures, artworks, and text paragraphs in printed documents. Moreover, the moire patterns on the segmented document components can be easily suppressed. The suppression yields better image quality for further analysis and image compression. Experimental results are shown to demonstrate the feasibility of the proposed approach.

Compression of Chinese Character Patterns in Document Images Based on Rectangular Region Partitioning Using Contour Information and Huffman Coding

Abstract: Document image compression is important in modern communication systems, and many lossless/lossy compression algorithms have been proposed for a variety of documents. In documents with Chinese characters, there are 5401 commonly used Chinese character patterns. It requires a large amount of space to store all Chinese character patterns of different fonts in computer systems. However, the compression of Chinese character patterns has not been extensively studied. In this paper, a new document image compression method is proposed. The purpose is to provide an effective encoding algorithm for Chinese character patterns and in the meantime obtain good compression results for general documents. The proposed method includes rectangular region partitioning, encoding of rectangular regions, and encoding of contour information. An input image is first partitioned into nonoverlapping blocks, and each block that contains black pixels is partitioned into nonoverlapping rectangles. The rectangles are then encoded in an effective fashion. For the purpose of lossless compression, contour information is exploited to encode the contour blocks with static Huffman coding. Experimental results showed that the proposed method is not only suitable for Chinese documents but also has good performance for general documents.