Joint Bilevel Image Experts Group Research Articles

Bi-level image compression technique using neural networks

This study presents the utilisation of neural-network for bi-level image compression. In the proposed lossy compression method, the locations of pixels of image are applied to the inputs of a multilayer perceptron neural-network. The output of the network denotes the pixel intensity (0 or 1). The final weights of the trained neural-network are quantised, represented by a few bits, Huffman encoded and then stored as the compressed image. In the decompression phase, by applying the pixels locations to the trained network, the output determines the intensity. The results of experiments on more than 4000 different images indicate higher compression rate of the proposed structure compared with the commonly used methods such as Comite Consultatif International Telephonique of Telegraphique (CCITT) G4 and joint bi-level image experts group (JBIG2) standards. Moreover, quantisation issue in neural-network deployment is addressed and a solution is proposed. Further, an adaptive technique based on binary image characteristics is applied to achieve more compression rates.

Bi-Level Image Compression Estimating the Markov Order of Dependencies

This paper presents a bi-level image compression method based on chain codes and entropy coders. However, the proposed method also includes an order estimation process to estimate the order of dependencies that may exist among the chain code symbols prior to the entropy coding stage. For each bi-level image, the method first obtains its chain code representation and then estimates its order of symbol dependencies. This order value is used to find the conditional and joint symbol probabilities corresponding to our newly defined Markov model. Our order estimation process is based on the Bayesian information criterion (BIC), a statistically based model selection technique that has proved to be a consistent order estimator. In our experiments, we show how our order estimation process can help achieve more efficient compression levels by providing comparisons against some of the most commonly used image compression standards such as the Graphics Interchange Format (GIF), Joint Bi-level Image Experts Group (JBIG), and JBIG2.

Efficient bilevel image compression by grouping symbols of chain coding techniques

We propose a procedure that can improve the coding efficiency of bilevel image compression schemes that are based on chain coding techniques and entropy coders. The two chain coding techniques analyzed in this study employ three and eight symbols, respectively, to code the outline of object shapes in bilevel images. Once we have the chain code representation of a contour, we group the original chain symbols in blocks of two or more symbols to build new extended alphabets from which we can obtain more efficient compression rates by using a statistical model that best represents our new alphabets and entropy coding. The coding efficiency of every new alphabet is measured by its entropy, average length in bits per original source symbol, and the total length in bits that represent the compressed image. We compare the compression efficiency between our two chain coding techniques using our three entropy coding parameters for symbol groupings of different sizes. In our experiments, we also show how our blocking approach produces an entropy value that is lower than its expected theoretical counterpart for a given order of symbol groupings and, as a consequence, the average and total lengths are expected to follow a decreasing pattern as well. Finally, we compare our best encoding model against the Joint Bilevel Image Experts Group's JBIG and JBIG2 bilevel image compression standards.

Morphological reconstruction of semantic layers in map images

Map images are composed of semantic layers depicted in arbitrary color. Color separation is often needed to divide the image into layers for storage and processing. Separation can result in severe artifacts because of the overlapping of the layers. In this work, we introduce a technique to restore the original semantic lay- ers after the color separation. The proposed restoration technique improves compression performance of the reconstructed layers in comparison to the corrupted ones when compressed by lossless algorithms such as International Communication Unit (ITU) Group 4 (TIFF G4), Portable Network Graphics (PNG), Joint Bi-level Image experts Group (JBIG), and context tree method. The resulting tech- nique also provides good visual quality of the reconstructed image layers, and can therefore be applied for selective layer removal/ extraction in other map processing applications, e.g., area

Compressing bilevel images by means of a three-bit chain code

We propose a lossless compression method based on a chain code composed of only three symbols. The method is applicable to compress 2-D binary object shapes, and it consists of representing the orthogonal direction changes of the discrete contour, corresponding to each object binary shape, by three bits of a chain code. According to our experimental results, we find that this method is suitable for the representation of bilevel images. The results are about 25% more efficient in compression than the Freeman chain code method, and an average of 29% better than the Joint Bilevel Image Experts Group (JBIG) compressor.

Near-lossless/lossless compression of error-diffused images using a two-pass approach

The current international standard, the Joint Bilevel Image Experts Group (JBIG), is a representative of a bilevel image compression algorithm. It compresses bilevel images with high performance, but it shows relatively low performance in compressing error-diffused halftone images. This paper proposes a new bilevel image compression for error-diffused images, which is based on Bayes' theorem. The proposed coding procedure consists of two passes. It groups 2 x 2 dots into a cell, where each cell is represented by the number of black dots and the locations of the black dots in the cell. The number of black dots in the cell is encoded in the first pass, and their locations are encoded in the second pass. The first pass performs a near-lossless compression, which can be refined to be lossless by the second pass. Experimental results show a high compression performance for the proposed method when it is applied to error-diffused images.

An algorithm for compression of bilevel images

This paper presents the block arithmetic coding for image compression (BACIC) algorithm: a new method for lossless bilevel image compression which can replace JBIG, the current standard for bilevel image compression. BACIC uses the block arithmetic coder (BAC): a simple, efficient, easy-to-implement, variable-to-fixed arithmetic coder, to encode images. BACIC models its probability estimates adaptively based on a 12-bit context of previous pixel values; the 12-bit context serves as an index into a probability table whose entries are used to compute p(1) (the probability of a bit equaling one), the probability measure BAC needs to compute a codeword. In contrast, the Joint Bilevel Image Experts Group (JBIG) uses a patented arithmetic coder, the IBM QM-coder, to compress image data and a predetermined probability table to estimate its probability measures. JBIG, though, has not get been commercially implemented; instead, JBIG's predecessor, the Group 3 fax (G3), continues to be used. BACIC achieves compression ratios comparable to JBIG's and is introduced as an alternative to the JBIG and G3 algorithms. BACIC's overall compression ratio is 19.0 for the eight CCITT test images (compared to JBIG's 19.6 and G3's 7.7), is 16.0 for 20 additional business-type documents (compared to JBIG's 16.0 and G3's 6.74), and is 3.07 for halftone images (compared to JBIG's 2.75 and G3's 0.50).

A JBIG-based approach to the encoding of contour maps

Recently, a new technique for the lossless encoding of contour maps was introduced, based on the concept of "transition points". In this paper, we show that by using a simple representation for the transition points, i.e., the map of transition points, the problem of encoding arbitrary contour maps can be easily converted into a problem of lossless image coding. Experimental results show that the Joint Bilevel Image Experts Group (JBIG) image coding standard can be used successfully for encoding maps of transition points, outperforming, in most cases, differential chain-coding. Moreover, if we take into account that JBIG codecs are available as "off-the-shelf" components, then the effort required to implement the proposed method is small.

The Qx-coder

The IBM Adaptive Bilevel Image Compression (ABIC) algorithm depends upon the hardware-optimized Q-coder. The Joint Bi-level Image Experts Group (JBIG) settled upon a software-optimized QM-coder. This paper explores the incompatibilities of the hardware-and software-optimized binary arithmetic coding conventions and reports on the solution that allowed a merged Qx-coder in hardware. A unique hardware solution is presented for the termination of the JBIG data stream (CLEARBITS). The probability estimation is presented in a common format. Detailed flowcharts are included in the Appendix. An ASIC core is available that supports both the ABIC and JBIG bilevel data compression standards using this merged Qx-coder.

Enhanced JBIG-based compression for satisfying objectives of engineering document management system

The main objectives of an engineering document manage- ment (EDM) system are outlined. Existing image compression algorithms (e.g., Group 4 (G4) and Joint Bilevel Image Experts Group (JBIG)) offer efficient solutions to the storage problem but do not sufficiently support other objectives such as spatial access and fast decoding. We propose a novel method based on JBIG in which the other objectives are also met. The compression performance of the proposed method is only 10% worse than that of JBIG, and at the same time, spatial access to a com- pressed file is achieved. The method is also 2.5 times faster in decom- pression than JBIG. This speedup is comparable to the G4 standard, but with better compression performance. The proposed method is appli- cable not only to engineering drawings but to binary images in any docu- ment imaging system. © 1998 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(98)02705-6)

Tree coding of bilevel images

Presently, sequential tree coders are the best general purpose bilevel image coders and the best coders of halftoned images. The current ISO standard, Joint Bilevel Image Experts Group (JBIG), is a good example. A sequential tree coder encodes the data by feeding estimates of conditional probabilities to an arithmetic coder. The conditional probabilities are estimated from co-occurrence statistics of past pixels, the statistics are stored in a tree. By organizing the code length calculations properly, a vast number of possible models (trees) reflecting different pixel orderings can be investigated within reasonable time prior to generating the code. A number of general-purpose coders are constructed according to this principle. Rissanen's one-pass algorithm, context, is presented in two modified versions. The baseline is proven to be a universal coder. The faster version, which is one order of magnitude slower than JBIG, obtains excellent and highly robust compression performance. A multipass free tree coding scheme produces superior compression results for all test images. A multipass free template coding scheme produces significantly better results than JBIG for difficult images such as halftones. By utilizing randomized subsampling in the template selection, the speed becomes acceptable for practical image coding.

The emerging JBIG2 standard

The Joint Bi-Level Image Experts Group (JBIG), an international study group affiliated with ISO/IEC and ITU-T, is in the process of drafting a new standard for lossy and lossless compression of bilevel images. The new standard, informally referred to as JBIG2, will support model-based coding for text and halftones to permit compression ratios up to three times those of existing standards for lossless compression. JBIG2 will also permit lossy preprocessing without specifying how it is to be done, In this case, compression ratios up to eight times those of existing standards may be obtained with imperceptible loss of quality. It is expected that JBIG2 will become an international standard by 2000.