Mixed Raster Content (MRC) is a standard for efficient document compression which can dramatically improve
the compression/quality tradeoff as compared to traditional lossy image compression algorithms. The key to
MRC's performance is the separation of the document into foreground and background layers, represented as
a binary mask. Typically, the foreground layer contains text colors, the background layer contains images and
graphics, and the binary mask layer represents fine detail of text fonts.
The resulting quality and compression ratio of a MRC document encoder is highly dependent on the segmentation
algorithm used to compute the binary mask. In this paper, we propose a novel segmentation method based
on the MRC standards (ITU-T T.44). The algorithm consists of two components: Cost Optimized Segmentation
(COS) and Connected Component Classification (CCC). The COS algorithm is a blockwise segmentation algorithm
formulated in a global cost optimization framework, while CCC is based on feature vector classification of
connected components. In the experimental results, we show that the new algorithm achieves the same accuracy
of text detection but with lower false detection of non-text features, as compared to state-of-the-art commercial
MRC products. This results in high quality MRC encoded documents with fewer non-text artifacts, and lower
The JBIG2 binary image encoder dramatically improves compression ratios over previous encoders. The effectiveness
of JBIG2 is largely due to its use of pattern matching techniques and symbol dictionaries for the
representation of text. While dictionary design is critical to achieving high compression ratios, little research
has been done in the optimization of dictionaries across stripes and pages.
In this paper we propose a novel dynamic dictionary design that substantially improves JBIG2 compression
ratios, particularly for multi-page documents. This dynamic dictionary updating scheme uses caching algorithms
to more effciently manage the symbol dictionary memory. Results show that the new dynamic symbol caching
technique outperforms the best previous dictionary construction schemes by between 13% and 46% for lossy
compression when encoding multi-page documents. In addition, we propose a fast and low-complexity pattern
matching algorithm that is robust to substitution errors and achieves high compression ratios.
A typical document image to be copied generally consists of various types of elements, for example, texts, halftone images, line drawings, and continuous tone images printed on background. To enhance the copy quality of such a compound document image, one or more image enhancement processes can be applied. However, it is not suitable to apply an enhancement method, which is probably appropriate to a typical kind of document element, on the document image entirely. Instead, it is preferable to discriminate each document element and to apply adequate image enhancement methods on respective document elements. The proposed document image enhancement method is comprised of a segmentation phase and an enhancement phase. In the segmentation phase, it classifies each pixel of the input document image into text, continuous tone image, halftone image and background by using a state transition machine, pixel-based features and run-based features. In the enhancement phase, it applies different enhancement methods on respective document elements. In this way, the dark gray texts are converted into black texts, and the edges of texts and continuous tone images are emphasized, while prohibiting halftone image regions from inducing erroneously enhanced artifacts.