This paper will describe the Mixed Raster Content (MRC) method for compressing compound images, containing both binary text and continuous-tone images. A single compression algorithm that simultaneously meets the requirements for both text and image compression has been elusive. MRC takes a different approach. Rather than using a single algorithm, MRC uses a multi-layered imaging model for representing the results of multiple compression algorithms, including ones developed specifically for text and for images. As a result, MRC can combine the best of existing or new compression algorithms and offer different quality-compression ratio tradeoffs. The algorithms used by MRC set the lower bound on its compression performance. Compared to existing algorithms, MRC has some image-processing overhead to manage multiple algorithms and the imaging model. This paper will develop the rationale for the MRC approach by describing the multilayered imaging model in light of a rate-distortion trade-off. Results will be presented comparing images compressed using MRC, JPEG and state-of-the-art wavelet algorithms such as SPIHT. MRC has been approved or proposed as an architectural model for several standards, including ITU Color Fax, IETF Internet Fax, and JPEG 2000. 1.

Abstract The main contribution of this work is a new paradigm for image representation and image compression. We describe a new multi-layered representation technique for images. An image is parsed into a superposition of coherent layers: smooth-regions layer, textures layer, etc. The multi-layered decomposition algorithm consists in a cascade of compressions applied successively to the image itself and to the residuals that resulted from the previous compressions. During each iteration of the algorithm, we code the residual part in a lossy way: we only retain the most significant structures of the residual part, which results in a sparse representation. Each layer is encoded independently with a different transform, or basis, at a different bitrate; and the combination of the compressed layers can always be reconstructed in a meaningful way. The strength of the multi-layer approach comes from the fact that different sets of basis functions complement each others: some of the basis functions will give reasonable account of the large trend of the data, while others will catch the local transients, or the oscillatory patterns. This multi-layered representation has a lot of beautiful applications in image understanding, and image and video coding. We have implemented the algorithm and we have studied its capabilities.

We present a document-specific OCR system and apply it to a corpus of faxed business letters. Unsupervised classification of the segmented character bitmaps on each page, using a "clump" metric, typically yields several hundred clusters with highly skewed populations. Letter identities are assigned to each cluster by maximizing matches with a lexicon of English words. We found that for 2/3 of the pages, we can identify almost 80% of the words included in the lexicon, without any shape training. Residual errors are caused by mis-segmentation including missed lines and punctuation. This research differs from earlier attempts to apply cipher decoding to OCR in (1) using real data (2) a more appropriate clustering algorithm, and (3) decoding a many-to-many instead of a one-to-one mapping between clusters and letters. 1.

DjVu is an image compression technique specifically geared towards the compression of scanned documents in color at high resolution. Typical magazine pages in color scanned at 300dpi are compressed to between 40 and 80 KB, or 5 to 10 times smaller than with JPEG for a similar level of subjective quality. The foreground layer, which contains the text and drawings and requires high spatial resolution, is separated from the background layer, which contains pictures and backgrounds and requires less resolution. The foreground is compressed with a bi-tonal image compression technique that takes advantage of character shape similarities. The background is compressed with a new progressive, wavelet-based compression method. A real-time, memory efficient version of the decoder is available as a plug-in for popular web browsers. 1

Compound document images contain graphic or textual content along with pictures. They are a very common form of documents, found in magazines, brochures, web sites, etc. We focus our attention on the mixed raster content (MRC) multilayer approach for compound image compression. We study block thresholding as a mean to segment an image for MRC. An attempt is made to optimize the block threshold in a rate-distortion sense. Also, a fast algorithm is presented to approximate the optimized method. Extensive results are presented including rate-distortion curves, segmentation masks and reconstructed images, showing the performance of the proposed algorithm.

The Mixed Raster Content (MRC) ITU document compression standard (T.44) specifies a multilayer decomposition model for compound documents into two contone image layers and a binary mask layer for independent compression. While T.44 does not recommend any procedure for decomposition, it does specify a set of allowable layer codecs to be used after decomposition. While T.44 only allows older standardized codecs such as JPEG/JBIG/G3/G4, higher compression could be achieved if newer contone and bi-level compression standards such as JPEG2000/JBIG2 were used instead. In this paper, we present a MRC compound document codec using JPEG2000 as the image layer codec and a layer decomposition scheme matched to JPEG2000 for efficient compression. JBIG still codes the mask. Noise removal routines enable efficient coding of scanned documents along with electronic ones. Resolution scalable decoding features are also implemented. The segmentation mask obtained from layer decomposition, serves to separate text and other features.

There is an accelerating demand to access the visual content of documents stored in historical and cultural archives. Availability of electronic imaging tools and effective image processing techniques makes it feasible to process the multimedia data in large databases. In this paper, a framework for content-based retrieval of historical documents in the Ottoman Empire archives is presented. The documents are stored as textual images,which are compressed by constructing a library of symbols occurring in a document, and the symbols in the original image are then replaced with pointers into the codebook to obtain a compressed representation of the image. The features in wavelet and spatial domain based on angular and distance span of shapes are used to extract the symbols. In order to make content-based retrieval in historical archives, a query is specified as a rectangular region in an input image and the same symbol-extraction process is applied to the query region. The queries are processed on the codebook of documents and the query images are identified in the resulting documents using the pointers in textual images. The querying process does not require decompression of images. The new content-based retrieval framework is also applicable to many other document archives using different scripts.

Abstract—We present a compound image compression algorithm for real-time applications of computer screen image transmission. It is called shape primitive extraction and coding (SPEC). Real-time image transmission requires that the compression algorithm should not only achieve high compression ratio, but also have low complexity and provide excellent visual quality. SPEC first segments a compound image into text/graphics pixels and pictorial pixels, and then compresses the text/graphics pixels with a new lossless coding algorithm and the pictorial pixels with the standard lossy JPEG, respectively. The segmentation first classifies image blocks into picture and text/graphics blocks by thresholding the number of colors of each block, then extracts shape primitives of text/graphics from picture blocks. Dynamic color palette that tracks recent text/graphics colors is used to separate small shape primitives of text/graphics from pictorial pixels. Shape primitives are also extracted from text/graphics blocks. All shape primitives from both block types are losslessly compressed by using a combined shape-based and palette-based coding algorithm. Then, the losslessly coded bitstream is fed into a LZW coder. Experimental results show that the SPEC has very low complexity and provides visually lossless quality while keeping competitive compression ratios. Index Terms—Compound image compression, compound image segmentation, palette-based coding, shape-based coding, shape primitive extraction. I.

We present a new image compression technique called “DjVu ” that is specifically geared towards the compression of scanned documents in color at high revolution. DjVu enable any screen connected to the Internet to access and display images of scanned pages while faithfully reproducing the font, color, drawings, pictures, and paper texture. With DjVu, a typical magazine page in color at 300dpi can be compressed down to between 40 to 60 KB, approximately 5 to 10 times better than JPEG for a similar level of subjective quality. A real-time, memory efficient version of the decoder is available as a plug-in for popular web browsers. 1.

This paper presents a new color document image segmentation system suitable for historical Arabic manuscripts. Our system is composed of a hybrid method which couple together background light intensity normalization algorithm and k-means clustering with maximum likelihood (ML) estimation, for foreground / background separation. Firstly, the background normalization algorithm performs separation between foreground and background. This foreground is used in later steps. Secondly, our algorithm proceeds on luminance and distort the contrast. These distortions are corrected with a gamma correction and contrast adjustment. Finally, the new enhanced foreground image is segmented to foreground/background on the basis of ML estimation. The initial parameters for the ML method are estimated by k-means clustering algorithm. The segmented image is used to produce a final restored document image. The techniques are tested on a set of Arabic historical manuscripts documents from the National Tunisian Library. The performance of the algorithm is demonstrated on by real color manuscripts distorted with show-through effects, uneven background color and localized spot.