Showing papers on "Lossless compression published in 1997"

Context-based, adaptive, lossless image coding

Abstract: We propose a context-based, adaptive, lossless image codec (CALIC). The codec obtains higher lossless compression of continuous-tone images than other lossless image coding techniques in the literature. This high coding efficiency is accomplished with relatively low time and space complexities. The CALIC puts heavy emphasis on image data modeling. A unique feature of the CALIC is the use of a large number of modeling contexts (states) to condition a nonlinear predictor and adapt the predictor to varying source statistics. The nonlinear predictor can correct itself via an error feedback mechanism by learning from its mistakes under a given context in the past. In this learning process, the CALIC estimates only the expectation of prediction errors conditioned on a large number of different contexts rather than estimating a large number of conditional error probabilities. The former estimation technique can afford a large number of modeling contexts without suffering from the context dilution problem of insufficient counting statistics as in the latter approach, nor from excessive memory use. The low time and space complexities are also attributed to efficient techniques for forming and quantizing modeling contexts.

Lossless compression of continuous-tone images via context selection, quantization, and modeling

Abstract: Context modeling is an extensively studied paradigm for lossless compression of continuous-tone images. However, without careful algorithm design, high-order Markovian modeling of continuous-tone images is too expensive in both computational time and space to be practical. Furthermore, the exponential growth of the number of modeling states in the order of a Markov model can quickly lead to the problem of context dilution; that is, an image may not have enough samples for good estimates of conditional probabilities associated with the modeling states. New techniques for context modeling of DPCM errors are introduced that can exploit context-dependent DPCM error structures to the benefit of compression. New algorithmic techniques of forming and quantizing modeling contexts are also developed to alleviate the problem of context dilution and reduce both time and space complexities. By innovative formation, quantization, and use of modeling contexts, the proposed lossless image coder has a highly competitive compression performance and yet remains practical.

Unbounded Length Contexts for PPM

Abstract: The PPM data compression scheme has set the performance standard in lossless compression of text throughout the past decade. PPM is a finite-context statistical modelling technique that can be viewed as blending together several fixed-order context models to predict the next character in the input sequence. This paper gives a brief introduction to PPM, and describes a variant of the algorithm, called PPM*, which exploits contexts of unbounded length. Although requiring considerably greater computational resources (in both time and space), this reliably achieves compression superior to the benchmark PPMC version. Its major contribution is that it shows that the full information available by considering all substrings of the input string can be used effectively to generate high-quality predictions. Hence, it provides a useful tool for exploring the bounds of compression.

A corpus for the evaluation of lossless compression algorithms

Abstract: A number of authors have used the Calgary corpus of texts to provide empirical results for lossless compression algorithms. This corpus was collected in 1987, although it was not published until 1990. The advances with compression algorithms have been achieving relatively small improvements in compression, measured using the Calgary corpus. There is a concern that algorithms are being fine-tuned to this corpus, and that small improvements measured in this way may not apply to other files. Furthermore, the corpus is almost ten years old, and over this period there have been changes in the kinds of files that are compressed, particularly with the development of the Internet, and the rapid growth of high-capacity secondary storage for personal computers. We explore the issues raised above, and develop a principled technique for collecting a corpus of test data for compression methods. A corpus, called the Canterbury corpus, is developed using this technique, and we report the performance of a collection of compression methods using the new corpus.

Robust image authentication method surviving JPEG lossy compression

Abstract: Image authentication verifies the originality of an image by detecting malicious manipulations. This goal is different from that of image watermarking which embeds into the image a signature surviving most manipulations. Existing methods for image authentication treat all types of manipulation equally (i.e., as unacceptable). However, some applications demand techniques that can distinguish acceptable manipulations (e.g., compression) from malicious ones. In this paper, we describe an effective technique for image authentication which can prevent malicious manipulations but allow JPEG lossy compression. The authentication signature is based on the invariance of the relationship between DCT coefficients of the same position in separate blocks of an image. This relationship will be preserved when these coefficients are quantized in a JPEG compression process. Our proposed method can distinguish malicious manipulations from JPEG lossy compression regardless of how high the compression ratio is. We also show that, in different practical cases, the design of authenticator depends on the number of recompression times and on whether the image is decoded into integral values in the pixel domain during the recompression process. Theoretical and experimental results indicate that this technique is effective for image authentication.

EEG data compression techniques

Abstract: Electroencephalograph (EEG) and Holter EEG data compression techniques which allow perfect reconstruction of the recorded waveform from the compressed one are presented and discussed. Data compression permits one to achieve significant reduction in the space required to store signals and in transmission time. The Huffman coding technique in conjunction with derivative computation reaches high compression ratios (on average 49% on Holter and 58% on EEG signals) with low computational complexity. By exploiting this result a simple and fast encoder/decoder scheme capable of real-time performance on a PC was implemented. This simple technique is compared with other predictive transformations, vector quantization, discrete cosine transform (DCT), and repetition count compression methods. Finally, it is shown that the adoption of a collapsed Huffman tree for the encoding/decoding operations allows one to choose the maximum codeword length without significantly affecting the compression ratio. Therefore, low cost commercial microcontrollers and storage devices can be effectively used to store long Holter EEG's in a compressed format.

PNG (Portable Network Graphics) Specification Version 1.0

Abstract: This document describes PNG (Portable Network Graphics), an extensible file format for the lossless, portable, well-compressed storage of raster images. PNG provides a patent-free replacement for GIF and can also replace many common uses of TIFF. Indexed-color, grayscale, and truecolor images are supported, plus an optional alpha channel. Sample depths range from 1 to 16 bits.

A lempel-ziv data compression technique utilizing a dictionary pre-filled with frequent letter combinations, words and/or phrases

Abstract: The adaptive compression technique improves the Lempel-Ziv (LZ) technique because it reduces the required storage space (18) and transmission time with transferring data (22). Pre-filled compression dictionaries (48) are utilized to resolve prior problems with the Lempel-Ziv technique where compression software starts with an empty compression dictionary and little compression is attained until dictionary has been filled with sequences common in the data being compared. The compression dictionary (48) is pre-filled with letter sequences, words and/or phrases that are commonly found in the compressed area. They may be used in the pre-filled dictionary where they are determined by sampling text data from the same text genre. Initially, multiple pre-filled dictionaries (52) may be utilized by the software, where the most appropriate dictionary for maximum compression is identified and used to compress current data. These modifications are made to any of the known Lempel-Ziv compression techniques based on the variants detailed in 1977 and 1978 articles by Ziv and Lempel.

Data compression using adaptive bit allocation and hybrid lossless entropy encoding

Abstract: A method and apparatus for adaptive bit allocation and hybrid lossless entropy encoding. The system includes three components: (1) a transform stage, (2) a quantization stage, and (3) a lossless entropy coder stage. The transform stage (1) uses a wavelet transform algorithm. The quantization stage (2) adaptively estimates values for parameters defining an approximation between quantization size and the logarithm of quantization error, and recursively calculates the optimal quantization size for each band to achieve a desired bit rate. The baseband and subbands are transformed into quantization matrices using the corresponding quantization sizes. The lossless entropy coder stage (3) uses the observation that the entropy property of run lengths of zero index values in the subband quantization matrices is different from the entropy property of non-zero indices. Each quantization matrix is parsed so that each non-zero index is extracted into a separate stream, and the remaining position information is parsed into an odd stream of run length values for "0" and an even stream of run length values for "1". These three streams are Huffman coded separately in conventional fashion.

The lossless compression of AVIRIS images by vector quantization

Abstract: The structure of hyperspectral images reveals spectral responses that would seem ideal candidates for compression by vector quantization. This paper outlines the results of an investigation of lossless vector quantization of 224-band Airborne/Visible Infrared imaging Spectrometer (AVIRIS) images. Various vector formation techniques are identified and suitable quantization parameters are investigated. A new technique, mean-normalized vector quantization (M-NVQ), is proposed which produces compression performances approaching the theoretical minimum compressed image entropy of 5 bits/pixel. Images are compressed from original image entropies of between 8.28 and 10.89 bits/pixel to between 4.83 and 5.90 bits/pixel.

Method, apparatus and system for compressing data

Abstract: An apparatus and method for image data compression performs a modified zero-tree coding on a range of absolute image values from the largest to a determined smaller absolute value, based upon file size or quality. If it is desired to maintain more detail in the image, then a vector quantizer codes the remaining values below this determined smaller value to zero, and lossless entropy coding is performed on the results of the two coding steps. The determined smaller value can be adjusted by examination of the histogram of the tree, or iteratively to meet a preselected compressed image size criterion or to meet a predefined level of image quality, as determined by any suitable metric. If the image to be compressed is in RGB color space, the apparatus converts the RGB image to a less redundant color space before commencing further processing.

TMW - a new method for lossless image compression

Abstract: We present a general purpose lossless greyscale image compression method TMW that is based on the use of linear predictors and implicit seg mentation In order to achieve competitive com pression the compression process is split into an analysis step and a coding step In the rst step a set of linear predictors and other parameters suitable for the image is calculated which is in cluded in the compressed le and subsequently used for the coding step This adaption allows TMW to perform well over a very wide range of im age types Other signi cant features of TMW are the use of a one parameter probability distribu tion probability calculations based on unquantized prediction values blending of multiple probability distributions instead of prediction values and im plicit image segmentation The method has applications beyond image com pression The work is also relevant to image seg mentation and image comparison For image compression the method has been compared to CALIC on a selection of test images and typically outperforms it by between and percent at the cost of considerably slower com pression In particular a bitrate of less than bpp has been achieved for the luminance band of the well known lenna image compared to bpp reported for CALIC in Wu

Integrating error detection into arithmetic coding

Abstract: Arithmetic coding for data compression has gained widespread acceptance as the right method for optimum compression when used with a suitable source model. A technique to implement error detection as part of the arithmetic coding process is described. Heuristic arguments are given to show that a small amount of extra redundancy can be very effective in detecting errors very quickly, and practical tests confirm this prediction.

A linear quadtree compression scheme for image encryption

Abstract: A private key encryption scheme for a two-dimensional image data is proposed in this work. This scheme is designed on the basis of lossless data compression principle. The proposed scheme is developed to have both data encryption and compression performed simultaneously. For the lossless data compression effect, the quadtree data structure is used to represent the image; for the encryption purpose, various scanning sequences of image data are provided. The scanning sequences comprise a private key for encryption. Twenty four possible combinations of scanning sequences are defined for accessing four quadrants, thereby making available 24 n × 4 n(n − 1) 2 possibilities to encode an image of resolution 2n × 2n. The security of the proposed encryption scheme therefore relies on the computational infeasibility of an exhaustive search approach. Three images of 512 × 512 pixels are used to verify the feasibility of the proposed scheme. The testing results and analysis demonstrate the characteristics of the proposed scheme. This scheme can be applied for problems of data storage or transmission in a public network.

Apparatus and method for hybrid compression of raster data

Abstract: From a raster page, patches of connected pixels of the same color are identified. Patches of at least a predetermined size, typically corresponding to text or line art objects, are subjected to a lossless compression. Patches below the predetermined size, typically corresponding to image or photo objects, are substantially subjected to a lossy compression. The patch predetermined size controls the mix of lossless and lossy compression procedures. Optimum compression is achieved by maximizing the lossless compression while attaining a targeted compression ratio. Various features include efficient recognition and encoding of patches, refined treatment of the boundaries between the lossless- and the lossy-compressed pixels, adaptive compression ratio control, and fail-safe compression provisions.

A segmentation-based lossless image coding method for high-resolution medical image compression

Abstract: Lossless compression techniques are essential in archival and communication of medical images. Here, a new segmentation-based lossless image coding (SLIC) method is proposed, which is based on a simple but efficient region growing procedure. The embedded region growing procedure produces an adaptive scanning pattern for the image with the help of a very-few-bits-needed discontinuity index map. Along with this scanning pattern, an error image data part with a very small dynamic range is generated. Both the error image data and the discontinuity index map data parts are then encoded by the Joint Bi-level Image Experts Group (JBIG) method. The SLIC method resulted in, on the average, lossless compression to about 1.6 b/pixel from 8 b, and to about 2.9 b/pixel from 10 b with a database of ten high-resolution digitized chest and breast images. In comparison with direct coding by JBIG, Joint Photographic Experts Group (JPEG), hierarchical interpolation (HINT), and two-dimensional Burg prediction plus Huffman error coding methods, the SLIC method performed better by 4% to 28% on the database used.

A video compression scheme with optimal bit allocation among segmentation, motion, and residual error

Abstract: We present a theory for the optimal bit allocation among quadtree (QT) segmentation, displacement vector field (DVF), and displaced frame difference (DFD). The theory is applicable to variable block size motion-compensated video coders (VBSMCVC), where the variable block sizes are encoded using the QT structure, the DVF is encoded by first-order differential pulse code modulation (DPCM), the DFD is encoded by a block-based scheme, and an additive distortion measure is employed. We derive an optimal scanning path for a QT that is based on a Hilbert curve. We consider the case of a lossless VBSMCVC first, for which we develop the optimal bit allocation algorithm using dynamic programming (DP). We then consider a lossy VBSMCVC, for which we use Lagrangian relaxation, and show how an iterative scheme, which employs the DP-based solution, can be used to find the optimal solution. We finally present a VBSMCVC, which is based on the proposed theory, which employs a DCT-based DFD encoding scheme. We compare the proposed coder with H.263. The results show that it outperforms H.263 significantly in the rate distortion sense, as well as in the subjective sense.

Method and apparatus for wavelet based data compression having adaptive bit rate control for compression of audio information

Abstract: The compression method utilizes a combination of both lossy and lossless compression to achieve significant compression while retaining very high subjective quality of the reconstructed or decompressed signal. Methods and apparatus for compression and decompression of digital audio data are provided. In one preferred embodiment, the compression method or apparatus employs an approximation of a psychoacoustic model for wavelet packet decomposition and has a bit rate control feedback loop particularly well suited to matching the output bit rate of the data compressor to the bandwidth capacity of a communication channel.

Band ordering in lossless compression of multispectral images

Abstract: In this paper, we consider a model of lossless image compression in which each band of a multispectral image is coded using a prediction function involving values from a previously coded band of the compression, and examine how the ordering of the bands affects the achievable compression. We present an efficient algorithm for computing the optimal band ordering for a multispectral image. This algorithm has time complexity O(n/sup -/) for an n-band image, while the naive algorithm takes time /spl Omega/(n!). A slight variant of the optimal ordering problem that is motivated by some practical concerns is shown to be NP-hard, and hence, computationally infeasible, in all cases except for the most trivial possibility. In addition, we report on our experimental findings using the algorithms designed in this paper applied to real multispectral satellite data. The results show that the techniques described here hold great promise for application to real-world compression needs.

Redundancy of the Lempel-Ziv incremental parsing rule

Abstract: The Lempel-Ziv codes are universal variable-to-fixed length codes that have become virtually standard in practical lossless data compression. For any given source output string from a Markov or unifilar source, we upper-bound the difference between the number of binary digits needed to encode the string and the self-information of the string. We use this result to demonstrate that for unifilar or Markov sources, the redundancy of encoding the first n letters of the source output with the Lempel-Ziv incremental parsing rule (LZ'78), the Welch modification (LZW), or a new variant is O((ln n)/sup -1/), and we upper-bound the exact form of convergence. We conclude by considering the relationship between the code length and the empirical entropy associated with a string.

A fast block-sorting algorithm for lossless data compression

Abstract: Summary form only given. Introduces a new transformation for block-sorting data compression methods. The transformation is similar to the one presented by Burrows and Wheeler, but avoids the drawbacks of uncertain runtime and low performance with large blocks. The cost is a small compression loss and a slower back transformation. In addition to that it is well suited for hardware implementation. Typical applications include real-time data recording, fast communication lines, on the fly compression and any other task requiring high throughput. The difference between this transformation and the original block-sort transformation is that the original transformation sorts on unlimited context, whereas this transformation sorts on limited context (typically a few bytes) and uses the position in the input block to determine the sort order in the case of equal contexts.

A modified OWA operator and its use in lossless DPCM image compression

Abstract: The ordered weighted averaging (OWA) operator of Yager was introduced to provide a method for aggregating several inputs which lies between the Max and Min operators. The fundamental aspect of the OWA operator is a reordering step in which the input arguments are re-arranged according to their actual relative value. In this paper we describe a modified OWA operator in which the input arguments are not re-arranged according to their actual relative values but rather according to their estimated relative values. We describe an unusual application of this operator to lossless image compression.

Lossless image compression by quantization feedback in a content-driven enhanced Laplacian pyramid

Abstract: In this paper, the effects of quantization noise feedback on the entropy of Laplacian pyramids are investigated. This technique makes it possible for the maximum absolute reconstruction error to be easily and strongly upper-bounded (near-lossless coding), and therefore, allows reversible compression. The entropy-minimizing optimum quantizer is obtained by modeling the first-order distributions of the differential signals as Laplacian densities, and by deriving a model for the equivalent memoryless entropy. A novel approach, based on an enhanced Laplacian pyramid, is proposed for the compression, either lossless or lossy, of gray-scale images. Major details are prioritized through a content-driven decision rule embedded in a uniform threshold quantizer with noise feedback. Lossless coding shows improvements over reversible Joint Photographers Expert Group (JPEG) and the reduced-difference pyramid schemes, while lossy coding outperforms JPEG, with a significant peak signal-to-noise ratio (PSNR) gain. Also, subjective quality is higher even at very low bit rates, due to the absence of the annoying impairments typical of JPEG. Moreover, image versions having resolution and SNR that are both progressively increasing are made available at the receiving end from the earliest retrieval stage on, as intermediate steps of the decoding procedure, without any additional cost.

Lossless and nearly lossless compression for high quality images

Abstract: While a losslessly compressed facsimile image might require 20,000 bytes of storage, a losslessly compressed color high resolution scan of the same sized document might require 200,000,000 bytes of storage. This factor of 10,000 in the image size necessitates more than just better compression, it requires a change in viewpoint about compression. A compression system for high quality images must provide a way to access only the required data rather than decompressing all the data and then selecting the desired portion. Furthermore, a high quality image compression system should be able to provide the best possible images for output devices which as of yet have not been manufactured. Finally, a high quality compression system should allow decompression and recompression without continual degradation of the image. This paper describes technologies including a reversible color transform, a reversible wavelet transform, a doubly embedded context mode, and a 'parseable' file format, which work together to provide solutions for high quality imaging needs.

Dictionary-based order-preserving string compression

Abstract: As no database exists without indexes, no index implementation exists without order-preserving key compression, in particular, without prefix and tail compression. However, despite the great potentials of making indexes smaller and faster, application of general compression methods to ordered data sets has advanced very little. This paper demonstrates that the fast dictionary-based methods can be applied to order-preserving compression almost with the same freedom as in the general case. The proposed new technology has the same speed and a compression rate only marginally lower than the traditional order-indifferent dictionary encoding. Procedures for encoding and generating the encode tables are described covering such order-related features as ordered data set restrictions, sensitivity and insensitivity to a character position, and one-symbol encoding of each frequent trailing character sequence. The experimental results presented demonstrate five-folded compression on real-life data sets and twelve-folded compression on Wisconsin benchmark text fields.

Fixed-slope universal lossy data compression

Abstract: Corresponding to any lossless codeword length function l, three universal lossy data compression schemes are presented: one is with a fixed rate, another is with a fixed distortion, and a third is with a fixed slope. The former two universal lossy data compression schemes are the generalization of Yang-Kieffer's (see ibid., vol.42, no.1, p.239-45, 1995) results to the general case of any lossless codeword length function l, whereas the third is new. In the case of fixed-slope /spl lambda/>0, our universal lossy data compression scheme works as follows: for any source sequence x/sup n/ of length n, the encoder first searches for a reproduction sequence y/sup n/ of length n which minimizes a cost function n/sup -1/l(y/sup n/)+/spl lambda//spl rho//sub n/(x/sup n/, y/sup n/) over all reproduction sequences of length n, and then encodes x/sup n/ into the binary codeword of length l(y/sup n/) associated with y/sup n/ via the lossless codeword length function l, where /spl rho//sub n/(x/sup n/, y/sup n/) is the distortion per sample between x/sup n/ and y/sup n/. Under some mild assumptions on the lossless codeword length function l, it is shown that when this fixed-slope data compression scheme is applied to encode a stationary, ergodic source, the resulting encoding rate per sample and the distortion per sample converge with probability one to R/sub /spl lambda// and D/sub /spl lambda//, respectively, where (D/sub /spl lambda//, R/sub /spl lambda//) is the point on the rate distortion curve at which the slope of the rate distortion function is -/spl lambda/. This result holds particularly for the arithmetic codeword length function and Lempel-Ziv codeword length function. The main advantage of this fixed-slope universal lossy data compression scheme over the fixed-rate (fixed-distortion) universal lossy data compression scheme lies in the fact that it converts the encoding problem to a search problem through a trellis and then permits one to use some sequential search algorithms to implement it. Simulation results show that this fixed-slope universal lossy data compression scheme, combined with a suitable search algorithm, is promising.

System and method for multirecord compression in a relational database

Abstract: The invention is a system and method for database compression which creates partial indexing into compressed sub table blocks of databases. Table rows with the same or related indexing parameters are grouped as "sub-table blocks" and are stored as compressed binary objects, with the indexing fields stored in the same row, external to the binary block. The binary object expands to multiple database rows when accessed via the sub table block interface, thus forming a hierarchical, pre-joined database organization. Mechanisms are provided for creating, accessing, and manipulating the data blocks, and a date-based versioning mechanism. The compression employed is the known Vdelta package, which operates at a byte level to provide a useful compromise between speed and compression efficiency, even for relative short compression blocks. In realistic tests, the I/O time gained through compression results in a time saving which exceeds the processing penalty. The overall compression ratio is data dependent, but in a realistic test it averages about 4.

Text Image Compression Using Soft Pattern Matching

Abstract: We present a method for both lossless and lossy compression of bi-level images that consist mostly of printed or typed text. The key feature of the method is soft pattern matching, a way of making use of the information in previously encountered characters without risking the introduction of character substitution errors. We can obtain lossless compression which is about 20% better than that of the JBIG standard by direct application of this method. By allowing some loss based partly on the pattern matching using a technique called selective pixel reversal, we can obtain compression ratios about 2-4 times the compression ratios of JBIG and 3-8 times those of G3 facsimile with no visible loss of quality. If used in facsimile machines, these compression improvements would translate directly into communication cost reductions of the same factors, or into the capability of transmitting images at higher resolution with no increase in the number of bits sent.

A theory for the optimal bit allocation between displacement vector field and displaced frame difference

Abstract: We address the fundamental problem of optimally splitting a video sequence into two sources of information, the displaced frame difference (DFD) and the displacement vector field (DVF). We first consider the case of a lossless motion-compensated video coder (MCVC), and derive a general dynamic programming (DP) formulation which results in an optimal tradeoff between the DVF and the DFD. We then consider the more important case of a lossy MCVC, and present an algorithm which solves the tradeoff between the rate and the distortion. This algorithm is based on the Lagrange multiplier method and the DP approach introduced for the lossless MCVC. We then present an H.263-based MCVC which uses the proposed optimal bit allocation, and compare its results to H.263. As expected, the proposed coder is superior in the rate-distortion sense. In addition to this, it offers many advantages for a rate control scheme. The presented theory can be applied to build new optimal coders, and to analyze the heuristics employed in existing coders. In fact, whenever one changes an existing coder, the proposed theory can be used to evaluate how the change affects its performance.