Improvement In Compression Ratio Research Articles

On Extending JPEG Compression Method Using Line-based Differential Coding

In this paper, we introduce a novel method to extend the JPEG standard, which is the most widely used for lossy image compression, in order to improve compression ratio. To employ two of the most successful methodologies for the data compression: differential coding and quantization simultaneously, we propose a line-based approach. For each line in a block, we apply one-dimensional discrete cosine transformation to the increments instead of the pixel values. Those values are quantized and entropy-coded similarly to the JPEG standard. To further increase compression ratio, the proposed method is plugged into the JPEG standard to form a new compression method, in which the proposed method are applied to only selected JPEG blocks. In our experiment, we found that the proposed method outperform the JPEG standard when the qualities of the coded images are set to be high. We believe the proposed method can be simply plugged into the standard to improve its compression ratio for higher quality images. 키워드: 영상처리,영상압축

Novel Lossless fMRI Image Compression Based on Motion Compensation and Customized Entropy Coding

We recently proposed a method for lossless compression of 4-D medical images based on the advanced video coding standard (H.264/AVC). In this paper, we present two major contributions that enhance our previous work for compression of functional MRI (fMRI) data: 1) a new multiframe motion compensation process that employs 4-D search, variable-size block matching, and bidirectional prediction; and 2) a new context-based adaptive binary arithmetic coder designed for lossless compression of the residual and motion vector data. We validate our method on real fMRI sequences of various resolutions and compare the performance to two state-of-the-art methods: 4D-JPEG2000 and H.264/AVC. Quantitative results demonstrate that our proposed technique significantly outperforms current state of the art with an average compression ratio improvement of 13%.

Residual DPCM about Motion Compensated Residual Signal for H.264 Lossless Coding

In this letter, a new Inter lossless coding method based on a residual DPCM (Differential Pulse Code Modulation) is proposed to improve compression ratio in the H.264 standard. Since the spatial correlation in a residual block can be further exploited among the residual signals after motion estimation/compensation, horizontal or vertical DPCM in the residual signals can be applied to further reduce the magnitudes of the residual signals. The proposed method reduces the average bitrates of 3.5% compared with the Inter lossless coding of the H.264 standard.

Compressing DNA sequence databases with coil

BackgroundPublicly available DNA sequence databases such as GenBank are large, and are growing at an exponential rate. The sheer volume of data being dealt with presents serious storage and data communications problems. Currently, sequence data is usually kept in large "flat files," which are then compressed using standard Lempel-Ziv (gzip) compression – an approach which rarely achieves good compression ratios. While much research has been done on compressing individual DNA sequences, surprisingly little has focused on the compression of entire databases of such sequences. In this study we introduce the sequence database compression software coil.ResultsWe have designed and implemented a portable software package, coil, for compressing and decompressing DNA sequence databases based on the idea of edit-tree coding. coil is geared towards achieving high compression ratios at the expense of execution time and memory usage during compression – the compression time represents a "one-off investment" whose cost is quickly amortised if the resulting compressed file is transmitted many times. Decompression requires little memory and is extremely fast. We demonstrate a 5% improvement in compression ratio over state-of-the-art general-purpose compression tools for a large GenBank database file containing Expressed Sequence Tag (EST) data. Finally, coil can efficiently encode incremental additions to a sequence database.Conclusioncoil presents a compelling alternative to conventional compression of flat files for the storage and distribution of DNA sequence databases having a narrow distribution of sequence lengths, such as EST data. Increasing compression levels for databases having a wide distribution of sequence lengths is a direction for future work.

Cascaded RLS–LMS Prediction in MPEG-4 Lossless Audio Coding

This paper describes the cascaded recursive least square-least mean square (RLS-LMS) prediction, which is part of the recently published MPEG-4 Audio Lossless Coding international standard. The predictor consists of cascaded stages of simple linear predictors, with the prediction error at the output of one stage passed to the next stage as the input signal. A linear combiner adds up the intermediate estimates at the output of each prediction stage to give a final estimate of the RLS-LMS predictor. In the RLS-LMS predictor, the first prediction stage is a simple first-order predictor with a fixed coefficient value 1. The second prediction stage uses the recursive least square algorithm to adaptively update the predictor coefficients. The subsequent prediction stages use the normalized least mean square algorithm to update the predictor coefficients. The coefficients of the linear combiner are then updated using the sign-sign least mean square algorithm. For stereo audio signals, the RLS-LMS predictor uses both intrachannel prediction and interchannel prediction, which results in a 3% improvement in compression ratio over using only the intrachannel prediction. Through extensive tests, the MPEG-4 Audio Lossless coder using the RLS-LMS predictor has demonstrated a compression ratio that is on par with the best lossless audio coders in the field. In this paper, the structure of the RLS-LMS predictor is described in detail, and the optimal predictor configuration is studied through various experiments.

Delivering health care on US$19 per capita

<h3>Abstract</h3> Much of the DNA and RNA sequencing data available is in the form of high-throughput sequencing (HTS) reads and is currently unindexed by established sequence search databases. Recent succinct data structures for indexing both reference sequences and HTS data, along with associated metadata, have been based on either hashing or graph models, but many of these structures are static in nature, and thus, not well-suited as backends for dynamic databases. We propose a parallel construction method for and novel application of the <i>wavelet trie</i> as a dynamic data structure for compressing and indexing graph metadata. By developing an algorithm for merging wavelet tries, we are able to construct large tries in parallel by merging smaller tries constructed concurrently from batches of data. When compared against general compression algorithms and those developed specifically for graph colors (VARI and Rainbowfish), our method achieves compression ratios superior to gzip and VARI, converging to compression ratios of 6.5% to 2% on data sets constructed from over 600 virus genomes. While marginally worse than compression by bzip2 or Rainbowfish, this structure allows for both fast extension and query. We also found that additionally encoding graph topology metadata improved compression ratios, particularly on data sets consisting of several mutually-exclusive reference genomes. It was also observed that the compression ratio of wavelet tries grew sublinearly with the density of the annotation matrices. This work is a significant step towards implementing a dynamic data structure for indexing large annotated sequence data sets that supports fast query and update operations. At the time of writing, no established standard tool has filled this niche.

MICRO: a new hybrid test data compression/decompression scheme

To overcome the limitation of the automatic test equipment (ATE), test data compression/decompression schemes become a more important issue of testing for a system-on-chip (SoC). In order to alleviate the limitation of previous works, a new hybrid test data compression/decompression scheme for an SoC is developed. The new scheme is based on analyzing the factors that influence test parameters: compression ratio and hardware overhead. To improve compression ratio, the proposed scheme, called the Modified Input reduction and CompRessing One block (MICRO), uses the modified input reduction, the one block compression, a novel mapping, and reordering algorithms. Unlike previous approaches using the cyclic scan register architecture, the proposed scheme is to compress original test data and to decompress the compressed test data without the cyclic scan register architecture. Therefore, the proposed scheme leads to high-compression ratio with low-hardware overhead. Experimental results on ISCAS '89 and ITC '99 benchmark circuits prove the efficiency of the new method

A novel DDS using nonlinear ROM addressing with improved compression ratio and quantization noise

This paper presents a novel direct digital frequency synthesis (DDFS) ROM compression technique based on two properties of a sine function: (a) piecewise linear technique to approximate a sinusoid, and (b) variation in the slope of the sinusoid at different phase angles. In the proposed DDFS architecture the ROM stores a few of the sinusoidal values, and the interpolation points between the successive stored values are calculated using linear and nonlinear addressing schemes. The nonlinear addressing scheme is used to adaptively vary the number of interpolation points as the slope of the sinusoid changes, leading to a greatly reduced ROM size. The proposed architecture achieves a high compression ratio with a spurious response comparable to that of recent ROM compression techniques. To validate the proposed DDS architecture, the linear, nonlinear, and conventional DDS ROM architectures were implemented in a Xilinx Spartan II FPGA and their spurious performances were compared.

Motion/Disparity Compensated Multiview Sequence Coding

A multiview sequence coding technique is proposed in this paper. A multiview sequence encoder must be able to reduce redundancies in both the time and view domains because the amount of data increases as the number of views (or cameras) increases. We define a new coding structure, a group of a group of pictures (GGOP), which is compatible with MPEG-2 and is flexible with a baseline distance. The GGOP could have several possible types, e.g., one-I type, two-I type, etc., according to the number of reference frame sequences. This permits one of these types to be selected according to the baseline distance among the cameras. In addition, the GGOP is designed for MPEG-2 compatibility. The proposed multiview sequence encoder consists of preprocessing stages, disparity estimation/compensation, motion estimation/compensation, residual coding, rate control, and entropy coding. It generates two types of bit streams, the main bit stream and the auxiliary bit stream. The main bit stream contains information concerning reference sequences including I-pictures, in order to maintain MPEG-2 compatibility. The auxiliary bit stream contains information concerning the remaining multiview sequences except for the sequences that include I-pictures. The proposed encoder shows improvements in compression ratio as well as in peak signal-to-noise ratio (PSNR) compared with conventional methods. The dependency of the GGOP on the baseline distance between cameras is also confirmed.

Evolvable hardware for lossless compression of very high resolution bi-level images

A compression method for image data with very high resolution, and an evolvable hardware chip implementing the process quickly are proposed. The current international standard for bi-level image coding, JBIG2, is modified by the proposed method to achieve high compression ratios, where compression parameters are optimised by search methods (primarily a genetic algorithm that has been extended and specialised for this problem). The results of computer simulations show a 23% improvement in compression ratios with the proposed method compared to JBIG2, and demonstrate that the optimisation process can be completed very quickly, even with software execution. The experiment shows that when the method is implemented by hardware with an evolvable hardware chip, the processing is dramatically faster than execution with software. Activities concerning ISO standardisation to adopt part of the technology used in this method to the JBIG2 standard are also described.

Compression of 2D Vector Fields Under Guaranteed Topology Preservation

Abstract In this paper we introduce a new compression technique for 2D vector fields which preserves the complete topology, i.e., the critical points and the connectivity of the separatrices. As the theoretical foundation of the algorithm, we show in a theorem that for local modifications of a vector field, it is possible to decide entirely by a local analysis whether or not the global topology is preserved. This result is applied in a compression algorithm which is based on a repeated local modification of the vector field ‐ namely a repeated edge collapse of the underlying piecewise linear domain. We apply the compression technique to a number of data sets with a complex topology and obtain significantly improved compression ratios in comparison to pre‐existing topology‐preserving techniques.

Effects of multiple-pass filtering in lossless predictive compression of waveform data

Presents the effects of the predictive filtering of waveform data in multiple passes as the first stage of a two-stage lossless compression algorithm. Predictive compression has a proven track record when applied to high dynamic range waveform data, wherein the waveform data are input to a linear predictor or perhaps an adaptive predictor for decorrelation, and the resultant residue is then subjected to an entropy coder to (ideally) represent the signal with a minimum number of bits. This compression is commonly applied with no loss of information. In this work, an adaptive filter is used for prediction, but instead of a single run through the predictor, the residue is continually passed back through the predictor in an attempt to further decorrelate the residue. Multiple passes of a gradient adaptive lattice filter has given the best decorrelation, yielding improved compression ratios. We run the compression technique on a seismic database, then provide some comparative lossless compression results using several coding schemes and show that using multiple-pass predictive filtering can improve the compression rates attainable.

Fast Random Access to Wavelet Compressed Volumetric Data Using Hashing

We present a new approach to lossy storage of the coefficients of wavelet transformed data. While it is common to store the coefficients of largest magnitude (and let all other coefficients be zero), we allow a slightly different set of coefficients to be stored. This brings into play a recently proposed hashing technique that allows space efficient storage and very efficient retrieval of coefficients. Our approach is applied to compression of volumetric data sets. For the ``Visible Man'' volume we obtain up to 80% improvement in compression ratio over previously suggested schemes. Further, the time for accessing a random voxel is quite competitive.

State-of-the-art techniques for lossless compression of 3D medical image sets

This paper explains the basic principles of lossless two-dimensional (2D) and three-dimensional (3D) image coding at a high level of abstraction. It discusses also a new inter-frame technique for lossless video coding based on intra-frame prediction and inter-frame context modelling. The performance of this technique is compared to that of state-of-the-art 2D coders on CT and MRI data sets from the Visual Human Project. The results show that the inter-frame technique outperforms state-of-the-art intra-frame coders, i.e. Calic and JPEG-LS. The improvement in compression ratio is significant in the case of CT data but is rather small in the case of MRI data.

SAW device implementation of a weighted stepped chirp code signal for direct sequence spread spectrum communications systems

This paper introduces a new weighted stepped chirp code signal for direct sequence spread spectrum (DS/SS) communications systems. This code signal uses the truncated cosine series functions as the chip functions, and it is the result of discretizing a continuous wave (CW) chirp that results in enhanced performance versus a pseudonoise (PN) code and equivalent performance and easier implementation than a CW chirp. This code signal will be shown to have improved compression ratio (CR) and peak sidelobe level (PSL) versus a PN code with identical code length and chip length. It also will be shown to have a similar CR and PSL compared to a CW chirp with identical pulse length and frequency deviation. The code signal is implemented on surface acoustic wave (SAW) devices that will be used as the code generator at the transmitter and the correlator at the receiver. The design considerations for the SAW device implementation of the code signal are discussed, including the effects of intersymbol interference. Experimental data is presented and compared to the predicted results for 8 different SAW devices examining the effects of code length (9 or 13 chips), weighting (uniform, cosine-squared, and Hamming), and sampling on the performance of the code signal.

Image compression using multiple transforms

Discrete cosine transform (DCT) is used in practically all commercial image and video compression systems. It has been found to be the best choice among all unitary transforms in compressing natural images. We are also quite convinced that it is difficult to find any clear alternative for DCT in typical transform coders. Instead, we decided to change the problem. If another single transform cannot outperform DCT, how about using a set of transforms? This paper tries to give an overview about this idea. It was tested in several experiments, which show that the use of several transforms can improve compression ratio despite the side information needed. The result is a compression method that can be described as an intermediate stage between traditional transform coding and vector quantization. It can be used for compressing natural images, but it would be suited best for real multidimensional data.

On‐line data compression and error analysis using wavelet technology

AbstractWavelet representation of a signal is efficient for process data compression. An on‐line compression algorithm based on Haar wavelets is proposed here. As a new data point arrives, the algorithm computes all the approximation coefficients and updates the multiresolution tree before it prepares to receive the next data point. An efficient bookkeeping and indexing scheme improves compression ratio more significantly than batch‐mode wavelet compression. Reconstruction algorithms and historian format for this bookkeeping are developed. Various analytical results on the bounds on compression ratio and sum of the square error that can be achieved using this algorithm are derived. Experimental evaluation over two sets of plant data shows that wavelet compression is superior to conventional interpolative methods (such as boxcar, backward slope, and SLIM3) in terms of quality of compression measured both in time and frequency domain and that the proposed on‐line wavelet compression algorithm performs better than the batch‐mode wavelet compression algorithm due to the efficient indexing and bookkeeping scheme. The on‐line algorithm combines the high quality of compression of wavelet‐based methods and on‐line implementation of interpolative compression algorithms at the same time.

Improving JPEG performance in conjunction with cloud editing for remote sensing applications

The authors propose an improved version of JPEG coding for compressing remote sensing images obtained by optical sensors onboard microsatellites. The approach involves expanding cloud features to include their cloud-land transitions, thereby simplifying their coding and subsequent compression. The system is fully automatic and appropriate for onboard implementation. Its improvement in coding stems from the realization that a large number of bits are used for coding the blocks that contain the transition regions between bright clouds, if present in the image, and the dark background. A fully automatic cloud-segmentation algorithm is therefore used to identify the external boundaries of the clouds, then smooth the corresponding blocks prior to coding. Further gains are also achieved by modifying the quantization table used for coding the coefficients of the discrete cosine transform. Compared to standard JPEG, at the same level of reconstruction quality, the new method can achieve compression ratio improvement by 13-161%, depending upon the context and the amount of cloud present in the specific image. The results are demonstrated with the help of several real images obtained by the University of Surrey, U.K., satellites.

ECG data compression using cut and align beats approach and 2-D transforms.

A new electrocardiogram (ECG) data compression method is presented which employs a two dimensional (2-D) transform. This 2-D transform method utilizes the fact that ECG signals generally show two types of redundancies--between adjacent heartbeats and between adjacent samples. A heartbeat data sequence is cut and beat-aligned to form a 2-D data array. Any 2-D compression method can then be applied. Transform coding using the 2-D discrete cosine transform (DCT) [2-D DCT] is employed here as an example. Using selections from the MIT-BIH arrhythmia and Medtronic databases, results are presented that illustrate substantial improvement in compression ratio over one-dimensional methods for comparable percent root-mean-square difference (PRD).

Area reduction and improvements in speed and compression ratio for a Lempel–Ziv data compression circuit using a content addressable memory

Area reduction and improvements in speed and compression ratio for a Lempel–Ziv data compression circuit using a content addressable memory