Efficient Compression Algorithm Research Articles

A High-Throughput Hardware Design of a One-Dimensional SPIHT Algorithm

Video display systems include frame memory, which stores video data for display. To reduce system cost, video data are often compressed for storage in frame memory. A desirable characteristic for display memory compression is support for the raster-scan processing order and the fixed target compression ratio. Set partitioning in hierarchical trees (SPIHT) is an efficient two-dimensional compression algorithm that guarantees a fixed target compression ratio, but its one-dimensional (1D) variation has received little attention, even though its 1D nature supports the raster-scan processing order. This paper proposes a novel hardware design for 1D SPIHT. The algorithm is modified to exploit parallelism for effective hardware implementation. For the encoder, dependences that prohibit parallel execution are resolved and a pipelined schedule is proposed. For the parallel execution of the decoder, the algorithm is modified to enable estimation of the bitstream length of each pass prior to decoding. This modification allows parallel and pipelined decoding operations, leading to a high-throughput design for both encoder and decoder. Although the modifications slightly decrease compression efficiency, additional optimizations are proposed to improve such efficiency. As a result, the peak signal-to-noise ratio drop is reduced from 1.40 dB to 0.44 dB. The throughputs of the proposed encoder and decoder are 7.04 Gbps and 7.63 Gbps, respectively, and their respective gate counts are 37.2 K and 54.1 K.

Efficient Scalable Image Compression Algorithms with Low Memory and Complexity

The set partitioning embedded block (SPECK) image compression algorithm has excellent performance, low computational complexity, and produces a rate scalable compressed bitstream that can be decoded efficiently at multiple bit-rates. Unfortunately, it consumes a huge amount of computer memory due to employing lists that store the coordinates of the image pixels and the coordinates of the sets that are generated during the coding process. In addition, it has complex memory management due to using an array of random access linked lists to store these sets according to their sizes. In this paper, we propose two algorithms that are based on SPECK. The main contribution of the first algorithm is that, as compared to SPECK, the amount of the algorithm’s usable memory is reduced to about 75% and at the same time its processing speed is increased and its rate distortion efficiency is preserved as will be demonstrated. The second algorithm has higher processing speed but has slightly lower rate distortion performance than the first algorithm.

Multispectral and Hyperspectral Lossless Compressor for Space Applications (HyLoC): A Low-Complexity FPGA Implementation of the CCSDS 123 Standard

An efficient compression of hyperspectral images on-board satellites is mandatory in current and future space missions in order to save bandwidth and storage space. Reducing the data volume in space is a challenge that has been faced with a twofold approach: to propose new highly efficient compression algorithms; and to present technologies and strategies to execute the compression in the hardware available on-board. The Consultative Committee for Space Data Systems (CCSDS), a consortium of the major space agencies in the world, has recently issued the CCSDS 123 standard for multispectral and hyperspectral image (MHI) compression, with the aim of facilitating the inclusion of on-board compression on satellites by the space industry. In this paper, we present a low-complexity feld programmable gate arrays (FPGAs) implementation of this recent CCSDS 123 standard, which demonstrates its main features in terms of compression efficiency and suitability for an implementation on the available on-board technologies. A hardware architecture is conceived and designed with the aim of achieving low hardware occupancy and high performance on a space-qualified FPGA from the Microsemi RTAX family. The resulting FPGA implementation is therefore suitable for on-board compression. The effect of the several CCSDS-123 configuration parameters on the compression efficiency and hardware complexity is taken into consideration to provide flexibility in such a way that the implementation can be adapted to different application scenarios. Synthesis results show a very low occupancy of 34% and a maximum frequency of 43 MHz on a space-qualified RTAX1000S. The benefits of the proposed implementation are further evidenced by a demonstrator, which is implemented on a commercial prototyping board from Xilinx. Finally, a comparison with other FPGA implementations of on-board data compression algorithms is provided.

Compression for trace zero points on twisted Edwards curves

Abstract We propose two optimal representations for the elements of trace zero subgroups of twisted Edwards curves. For both representations, we provide efficient compression and decompression algorithms. The efficiency of the algorithm is compared with the efficiency of similar algorithms on elliptic curves in Weierstrass form.

Segmentation-based compression scheme for 3D animated models

This paper presents an efficient compression algorithm for animated three-dimensional (3D) meshes. First, a segmentation approach is applied to achieve the motion estimation. The main idea is to exploit the temporal coherence of the geometry component by using the heat diffusion properties. The motion of the resulting regions is accurately described by 3D affine transforms. These transforms are computed at the first frame to match the subsequent ones. Second, in order to achieve a good compression performance, an efficient rate control mechanism is proposed to quantize the temporal prediction errors. At this stage, a rate-distortion model is used for quantizing the residual information. Comparative coding tests, for irregular 3D mesh sequences, were conducted to evaluate the coding efficiency of the proposed compression scheme. Simulation results show very promising performances.

Lightweight Lossy Compression of Biometric Patterns via Denoising Autoencoders

Wearable Internet of Things (IoT) devices permit the massive collection of biosignals (e.g., heart-rate, oxygen level, respiration, blood pressure, photo-plethysmographic signal, etc.) at low cost. These, can be used to help address the individual fitness needs of the users and could be exploited within personalized healthcare plans. In this letter, we are concerned with the design of lightweight and efficient algorithms for the lossy compression of these signals. In fact, we underline that compression is a key functionality to improve the lifetime of IoT devices, which are often energy constrained, allowing the optimization of their internal memory space and the efficient transmission of data over their wireless interface. To this end, we advocate the use of autoencoders as an efficient and computationally lightweight means to compress biometric signals. While the presented techniques can be used with any signal showing a certain degree of periodicity, in this letter we apply them to ECG traces, showing quantitative results in terms of compression ratio, reconstruction error and computational complexity. State of the art solutions are also compared with our approach.

Resource efficient data compression algorithms for demanding, WSN based biomedical applications

During the last few years, medical research areas of critical importance such as Epilepsy monitoring and study, increasingly utilize wireless sensor network technologies in order to achieve better understanding and significant breakthroughs. However, the limited memory and communication bandwidth offered by WSN platforms comprise a significant shortcoming to such demanding application scenarios. Although, data compression can mitigate such deficiencies there is a lack of objective and comprehensive evaluation of relative approaches and even more on specialized approaches targeting specific demanding applications. The research work presented in this paper focuses on implementing and offering an in-depth experimental study regarding prominent, already existing as well as novel proposed compression algorithms. All algorithms have been implemented in a common Matlab framework. A major contribution of this paper, that differentiates it from similar research efforts, is the employment of real world Electroencephalography (EEG) and Electrocardiography (ECG) datasets comprising the two most demanding Epilepsy modalities. Emphasis is put on WSN applications, thus the respective metrics focus on compression rate and execution latency for the selected datasets. The evaluation results reveal significant performance and behavioral characteristics of the algorithms related to their complexity and the relative negative effect on compression latency as opposed to the increased compression rate. It is noted that the proposed schemes managed to offer considerable advantage especially aiming to achieve the optimum tradeoff between compression rate-latency. Specifically, proposed algorithm managed to combine highly completive level of compression while ensuring minimum latency thus exhibiting real-time capabilities. Additionally, one of the proposed schemes is compared against state-of-the-art general-purpose compression algorithms also exhibiting considerable advantages as far as the compression rate is concerned.

Efficient layout data compression algorithm and its low-complexity, high-performance hardware decoder implementation for multiple electron-beam direct-write systems

A lossless electron-beam layout (EBL) data compression algorithm, LineDiff Entropy v2.0, and its low-complexity, high-performance hardware decoder for multiple electron-beam direct-write lithography systems are proposed. The algorithm compares consecutive data scanlines and encodes the data based on the change/no-change of pixel values and the lengths of pixel sequences. Then, the compaction steps of data omission, merging, and encoding of consecutive long identical scanlines are applied. Unique short codes are assigned to data with high occurrence frequency. The hardware decoder is designed as three circuit blocks that perform entropy decoding, decompaction, and EBL data generation through parallel outputs. The results demonstrate that our algorithm can achieve excellent compression performance and that the hardware decoder can decompress data at very high data rates.

Lossless compression of medical images using Burrows-Wheeler Transformation with Inversion Coder.

Medical imaging is a quickly growing industry where the need for highly efficient lossless compression algorithms is necessary in order to reduce storage space and transmission rates for the large, high resolution, medical images. Due to the fact that medical imagining cannot utilize lossy compression, in the event that vital information may be lost, it is imperative that lossless compression be used. While several authors have investigated lossless compression of medical images, the Burrows-Wheeler Transformation with an Inversion Coder (BWIC) has not been examined. Our investigation shows that BWIC runs in linear time and yields better compression rates than well-known image coders, such as JPEG-LS and JPEG-2000.

Logarithmical hopping encoding: a low computational complexity algorithm for image compression

LHE (logarithmical hopping encoding) is a computationally efficient image compression algorithm that exploits the Weber–Fechner law to encode the error between colour component predictions and the actual value of such components. More concretely, for each pixel, luminance and chrominance predictions are calculated as a function of the surrounding pixels and then the error between the predictions and the actual values are logarithmically quantised. The main advantage of LHE is that although it is capable of achieving a low-bit rate encoding with high quality results in terms of peak signal-to-noise ratio (PSNR) and image quality metrics with full-reference (FSIM) and non-reference (blind/referenceless image spatial quality evaluator), its time complexity is O(n) and its memory complexity is O(1). Furthermore, an enhanced version of the algorithm is proposed, where the output codes provided by the logarithmical quantiser are used in a pre-processing stage to estimate the perceptual relevance of the image blocks. This allows the algorithm to downsample the blocks with low perceptual relevance, thus improving the compression rate. The performance of LHE is especially remarkable when the bit per pixel rate is low, showing much better quality, in terms of PSNR and FSIM, than JPEG and slightly lower quality than JPEG-2000 but being more computationally efficient.

ERGC: an efficient referential genome compression algorithm.

Genome sequencing has become faster and more affordable. Consequently, the number of available complete genomic sequences is increasing rapidly. As a result, the cost to store, process, analyze and transmit the data is becoming a bottleneck for research and future medical applications. So, the need for devising efficient data compression and data reduction techniques for biological sequencing data is growing by the day. Although there exists a number of standard data compression algorithms, they are not efficient in compressing biological data. These generic algorithms do not exploit some inherent properties of the sequencing data while compressing. To exploit statistical and information-theoretic properties of genomic sequences, we need specialized compression algorithms. Five different next-generation sequencing data compression problems have been identified and studied in the literature. We propose a novel algorithm for one of these problems known as reference-based genome compression. We have done extensive experiments using five real sequencing datasets. The results on real genomes show that our proposed algorithm is indeed competitive and performs better than the best known algorithms for this problem. It achieves compression ratios that are better than those of the currently best performing algorithms. The time to compress and decompress the whole genome is also very promising. The implementations are freely available for non-commercial purposes. They can be downloaded from http://engr.uconn.edu/∼rajasek/ERGC.zip. rajasek@engr.uconn.edu.

A new wavelet based efficient image compression algorithm using compressive sensing

We propose a new algorithm for image compression based on compressive sensing (CS). The algorithm starts with a traditional multilevel 2-D Wavelet decomposition, which provides a compact representation of image pixels. We then introduce a new approach for rearranging the wavelet coefficients into a structured manner to formulate sparse vectors. We use a Gaussian random measurement matrix normalized with the weighted average Root Mean Squared energies of different wavelet subbands. Compressed sampling is finally performed using this normalized measurement matrix. At the decoding end, the image is reconstructed using a simple l1-minimization technique. The proposed wavelet-based CS reconstruction, with the normalized measurement matrix, results in performance increase compared to other conventional CS-based techniques. The proposed approach introduces a completely new framework for using CS in the wavelet domain. The technique was tested on different natural images. We show that the proposed technique outperforms most existing CS-based compression methods.

Compression of ECG Signal Based on Compressive Sensing and the Extraction of Significant Features

Diagnoses of heart diseases can be done effectively on long term recordings of ECG signals that preserve the signals’ morphologies. In these cases, the volume of the ECG data produced by the monitoring systems grows significantly. To make the mobile healthcare possible, the need for efficient ECG signal compression algorithms to store and/or transmit the signal efficiently has been rising exponentially. Currently, ECG signal is acquired at Nyquist rate or higher, thus introducing redundancies between adjacent heartbeats due to its quasi-periodic structure. Existing compression methods remove these redundancies by achieving compression and facilitate transmission of the patient’s imperative information. Based on the fact that these signals can be approximated by a linear combination of a few coefficients taken from different basis, an alternative new compression scheme based on Compressive Sensing (CS) has been proposed. CS provides a new approach concerned with signal compression and recovery by exploiting the fact that ECG signal can be reconstructed by acquiring a relatively small number of samples in the “sparse” domains through well-developed optimization procedures. In this paper, a single-lead ECG compression method has been proposed based on improving the signal sparisty through the extraction of the signal significant features. The proposed method starts with a preprocessing stage that detects the peaks and periods of the Q, R and S waves of each beat. Then, the QRS-complex for each signal beat is estimated. The estimated QRS-complexes are subtracted from the original ECG signal and the resulting error signal is compressed using the CS technique. Throughout this process, DWT sparsifying dictionaries have been adopted. The performance of the proposed algorithm, in terms of the reconstructed signal quality and compression ratio, is evaluated by adopting DWT spatial domain basis applied to ECG records extracted from the MIT-BIH Arrhythmia Database. The results indicate that average compression ratio of 11:1 with PRD1 = 1.2% are obtained. Moreover, the quality of the retrieved signal is guaranteed and the compression ratio achieved is an improvement over those obtained by previously reported algorithms. Simulation results suggest that CS should be considered as an acceptable methodology for ECG compression.

Efficient sampling for radar sensor networks

Compressive sensing (CS) is an excellent technique for data acquisition and reconstruction in radar sensor networks (RSNs) with a high computational capability. This paper presents a new efficient and effective signal compression and reconstruction algorithm based on CS principles for applications in real–world RSNs, in which the signals are obtained in real–world experiment of RSNs. The proposed algorithm neither requires any new optimisation method, nor needs complex pre–processing before compression. This method considers correlation between radar sensor signals to reduce the number of samples required for reconstruction of the original radar signals. We compare our algorithm's performance and complexity with some existing work, such as joint PCA & CS, DCS, and traditional CS. Numerical results show that the proposed algorithm performs more efficiently and effectively without introducing any more computation complexity. With more sensor nodes, our algorithm is more efficient, which significantly reduces the number of samples required per sensor.

An Efficient Compression Algorithm for Uncertain Databases Aimed at Mining Problems

An Efficient Compression Algorithm for Uncertain Databases Aimed at Mining Problems

Optimized Grid Based e-Learning Framework

E-Learning is the process of extending the resources to different locations by using multimedia communications. Many e-Learning methodologies are available and based on client-server, peer-to-peer and using Grid Computing concepts. To establish e-Learning process, systems should satisfy these needs, i) high storage for storing, ii) high network throughput for faster transfer and iii) efficient streaming of materials. The first and second needs are satisfied by using Grid and P2P technologies and the third need can be achieved by an efficient video compression algorithm. This study proposes a framework, called Optimized Grid Based e-Learning (OgBeL), which adopts both Grid and P2P technology. To reduce the e-Learning material size for efficient streaming, a light weight compression algorithm called dWave is embedded in OgBeL. The behavior of framework is analyzed in terms of time taken to transfer files using in-use grid protocols and in networks combined with grid and P2P.

Empirical Wavelet Transform Based ECG Signal Compression

ABSTRACTTransmission of biomedical signals over telephone lines or other communication channels is currently an important issue for the telemedicine applications. An efficient compression algorithm is needed to achieve a reduced information rate, for the storage and transmission purposes. In this paper, empirical wavelet transform (EWT) along with discrete wavelet transform (DWT) has been used for compression and reconstruction of the ECG signals. Key point lies in using different threshold for different modes obtained by applying EWT. Proposed algorithm has been tested on self-acquired (on BIOPAC®MP150) ECG signals of 20 subjects, each of 12 minutes duration and 360,000 samples with sampling rate 500 Hz and average 31.2 compression ratio (CR) and 3.28% percentage ratio distortion (PRD) have been obtained. Algorithm has also been applied on all 48 arrhythmia signals of Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH)77777\\+766 database, each of 30 minutes duration and 650,000 samples, sampled at 360 Hz and an average 33.1 CR and 3.3% PRD are obtained.

ECG compression retaining the best natural basis k-coefficients via sparse decomposition

A novel and efficient signal compression algorithm aimed at finding the sparsest representation of electrocardiogram (ECG) signals is presented and analyzed. The idea behind the method relies on basis elements drawn from the initial transitory of a signal itself, and the sparsity promotion process applied to its subsequent blocks grabbed by a sliding window. The saved coefficients rescaled in a convenient range, quantized and compressed by a lossless entropy-based algorithm.Experiments on signals extracted from the MIT-BIH Arrhythmia database show that the method achieves in most of the cases very high performance.

Improved Listless Embedded Block Partitioning Algorithms for Image Compression

In this paper, two simple yet efficient embedded block-based image compression algorithms are presented. These algorithms not only improve the rate distortion performances of set partitioning in hierarchical trees (SPIHT) and set partitioning in embedded block coder (SPECK) at lower bit rates but also reduces the dynamic memory requirement by 91.1% in comparison to SPIHT. The former objective is achieved by better exploiting the coefficient decaying spectrum of the wavelet transformd images and the later objective is realised by improved listless implementation of the algorithms. The proposed algorithms explicitly perform breadth first search like SPECK. Extensive simulation conducted on various standard grayscale and color images indicate significant peak-signal-to-noise-ratio (PSNR) improvement over most of the state-of-the-art wavelet-based embedded coders including JPEG2000 at lower rates. The reduction of encoding and decoding time as well as improvement in coding efficiency at lower bit rates facilitate these coder as better candidates for multimedia applications.

Efficient lossless codec for still color images with backward coding of wavelet trees

Despite the approval of a new standard in 2009, JPEG-extended range lossless, current digital products still employ previous standards for lossless image compression, such as JPEG, JPEG2000, JPEG-LS, etc. Wavelet-based codecs can provide abundant functionalities and excellent compression efficiency. Among them, the backward coding of wavelet trees (BCWT) algorithm offers lower complexity and consumes less internal buffer memory without sacrificing quality at similar compression ratios (CR) when compared to other wavelet-based codecs, such as JPEG2000 and set partitioning in hierarchical trees (SPIHT). A line-based BCWT was developed for further reduction of internal buffer memory. A very efficient line-based lossless BCWT compression algorithm is presented. Lossless color and lossless wavelet transform are employed and the original BCWT algorithm is modified for lossless operation, including incorporation of adaptive arithmetic coding. In order to eliminate coding redundancies, a set to zeros method and a zero tree detection algorithm are proposed, which significantly enhance the boundary condition CR performance while reserving the algorithm’s advantages. Tests and analysis results show that the lossless BCWT algorithm requires less memory and computational resources than SPIHT and JPEG2000, while retaining image quality comparable to the standard image codecs, therefore, lossless BCWT is quite suitable for implementation in modern digital technologies.