A technique for lossy compression of error-diffused halftones

Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2003-2004 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Near-Lossless Bitonal Image Compression System

The main purpose of this thesis is to develop an efficient near-lossless bitonal compression algorithm and to implement that algorithm on a hardware platform. The current methods for compression of bitonal images include the JBIG and JBIG2 algorithms, however both JBIG and JBIG2 have their disadvantages. Both of these algorithms are covered by patents filed by IBM, making them costly to implement commercially. Also, JBIG only provides means for lossless compression while JBIG2 provides lossy methods only for document-type images. For these reasons a new method for introducing loss and controlling this loss to sustain quality is developed. The lossless bitonal image compression algorithm used for this thesis is called Block Arithmetic Coder for Image Compression (BACIC), which can efficiently compress bitonal images. In this thesis, loss is introduced for cases where better compression efficiency is needed. However, introducing loss in bitonal images is especially difficult, because pixels undergo such a drastic change, either from white to black or black to white. Such pixel flipping introduces salt and pepper noise, which can be very distracting when viewing an image. Two methods are used in combination to control the visual distortion introduced into the image. The first is to keep track of the error created by the flipping of pixels, and using this error to decide whether flipping another pixel will cause the visual distortion to exceed a predefined threshold. The second method is region of interest consideration. In this method, lower loss or no loss is introduced into the important parts of an image, and higher loss is introduced into the less important parts. This allows for a good quality image while increasing the compression efficiency. Also, the ability of BACIC to compress grayscale images is studied and BACICm, a multiplanar BACIC algorithm, is created. A hardware implementation of the BACIC lossless bitonal image compression algorithm is also designed. The hardware implementation is done using VHDL targeting a Xilinx FPGA, which is very useful, because of its flexibility. The programmed FPGA could be included in a product of the facsimile or printing industry to handle the compression or decompression internal to the unit, giving it an advantage in the marketplace

JBIG2 Supported by OCR

Digital Mathematical libraries contain a large volume of PDF documents containing scanned text. In this paper, we describe how this documents can be compressed and thus provide them more effectively to the users. We introduce a JBIG2 standard for compressing bitonal images such as scanned text and we discuss issues if OCR is used for improving the compression ratio of jbig2enc open-source encoder. For this purpose, we have designed API for using OCR in jbig2enc which we describe in this paper together with already achieved results.Digitální matematické knihovnz obsahují velké množství PDF dokumentů obsahujících skenovaný text. V tomto článku popisujeme, jakým způsobem mohou být takové dokumenty komprimovány, a tím pádem poskytovány uživateli efektivnější cestou. Za tímto účelem představujeme JBIG2 standard pro kompresi bitonálních obrázků (např. naskenovaný text) a diskutujeme přínosy a problémy použití OCR za účelem zvýšení komprese volně šiřitelného jbig2enc enkodéru. Za tímto účelem jsme navrhli a implementovali rozhraní pro používání OCR v jbig2enc enkodéru, které zde popisujeme spolu s předběžnými výsledky.Digital Mathematical libraries contain a large volume of PDF documents containing scanned text. In this paper, we describe how this documents can be compressed and thus provide them more effectively to the users. We introduce a JBIG2 standard for compressing bitonal images such as scanned text and we discuss issues if OCR is used for improving the compression ratio of jbig2enc open-source encoder. For this purpose, we have designed API for using OCR in jbig2enc which we describe in this paper together with already achieved results

EFFICIENT IMAGE COMPRESSION AND DECOMPRESSION ALGORITHMS FOR OCR SYSTEMS

This paper presents an efficient new image compression and decompression methods for document images, intended for usage in the pre-processing stage of an OCR system designed for needs of the “Nikola Tesla Museum” in Belgrade. Proposed image compression methods exploit the Run-Length Encoding (RLE) algorithm and an algorithm based on document character contour extraction, while an iterative scanline fill algorithm is used for image decompression. Image compression and decompression methods are compared with JBIG2 and JPEG2000 image compression standards. Segmentation accuracy results for ground-truth documents are obtained in order to evaluate the proposed methods. Results show that the proposed methods outperform JBIG2 compression regarding the time complexity, providing up to 25 times lower processing time at the expense of worse compression ratio results, as well as JPEG2000 image compression standard, providing up to 4-fold improvement in compression ratio. Finally, time complexity results show that the presented methods are sufficiently fast for a real time character segmentation system

A new highly efficient algorithm for lossless binary image compression.

No abstract available.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b131703