Efficient Compression Method Research Articles

An efficient machine tool control instruction compression method for networked numerical control systems

With the development of computer and information technology, function modules of computer numerical control (CNC) systems are moving up to network environment to simplify local machine tool controller architecture and increase function reusability. In this context, network-based CNC system generates machine control instructions (MCI) in network environment and send MCI to local machines. Compared with building a remote real-time communication channel between networked CNC and local machines, applying offline interpolator is more feasible under state-of-the-art hardware and software. However, the size of MCI generated by offline interpolator is large. Transmitting MCI without compression will be inefficient and increase lead time. Currently there has been few researches on the features and compression of offline MCI. In this paper, a simple and effective compression method for MCI named multichannel differential run-length encoding (MDRL) is proposed. Utilizing the limited variation and high repetition features of MCI, MDRL algorithm first calculates the differences of MCI, then encodes the amplitudes and repetition of difference values using fixed length codes. Compression performance discrepancy among different channels is controlled using compressed data chunks. The decompression process of MDRL only needs a few hundred of bytes of space and can process in real-time. Compression performance of MDRL is tested using three common manufacturing tasks under different system configurations. The results show that the mean compression ratio of MDRL is 3.41%-15.86%. The advantages of MDRL are further validated by comparing with three other data compression algorithms. By using MDRL, MCI transmission efficiency can be significantly increased for networked CNC systems to increase function reusability and shorten lead time.

An Efficient Compression Method of Hyperspectral Images Based on Compressed Sensing and Joint Optimization

Compressed sensing provides the possibility of efficient compression of massive hyperspectral data. However, the existing methods often use the organization pattern of image blocks in sparse representation, and cannot make full use of inter spectral correlation. The separate retrieval of dictionary and measurement matrix also restricts the processing efficiency. To solve these problems, a novel and efficient hyperspectral image compression method based on compressed sensing and joint optimization is proposed in this paper. In sparse representation, the data organization pattern on spectral dimension is adopted to better express the correlation between spectra and improve the efficiency of operation. On the dictionary and measurement matrix, a joint optimization algorithm is proposed, which synchronously inhibits the sparse representation error and the reconstruction error. Experimental results show that compared with similar methods, the reconstruction error of this method is increased by 3 dB and the number of iterations is reduced by seven times, and the compression rate can reach 1/18.

Steerable-Discrete-Cosine-Transform (SDCT): Hardware Implementation and Performance Analysis.

In the last years, the need for new efficient video compression methods grown rapidly as frame resolution has increased dramatically. The Joint Collaborative Team on Video Coding (JCT-VC) effort produced in 2013 the H.265/High Efficiency Video Coding (HEVC) standard, which represents the state of the art in video coding standards. Nevertheless, in the last years, new algorithms and techniques to improve coding efficiency have been proposed. One promising approach relies on embedding direction capabilities into the transform stage. Recently, the Steerable Discrete Cosine Transform (SDCT) has been proposed to exploit directional DCT using a basis having different orientation angles. The SDCT leads to a sparser representation, which translates to improved coding efficiency. Preliminary results show that the SDCT can be embedded into the HEVC standard, providing better compression ratios. This paper presents a hardware architecture for the SDCT, which is able to work at a frequency of 188, reaching a throughput of 3.00 GSample/s. In particular, this architecture supports 8k UltraHigh Definition (UHD) (7680 × 4320) with a frame rate of 60 , which is one of the best resolutions supported by HEVC.

Sketching algorithms for genomic data analysis and querying in a secure enclave.

Genome-wide association studies (GWAS), especially on rare diseases, may necessitate exchange of sensitive genomic data between multiple institutions. Since genomic data sharing is often infeasible due to privacy concerns, cryptographic methods, such as secure multiparty computation (SMC) protocols, have been developed with the aim of offering privacy-preserving collaborative GWAS. Unfortunately, the computational overhead of these methods remain prohibitive for human-genome-scale data. Here we introduce SkSES (https://github.com/ndokmai/sgx-genome-variants-search), a hardware-software hybrid approach for privacy-preserving collaborative GWAS, which improves the running time of the most advanced cryptographic protocols by two orders of magnitude. The SkSES approach is based on trusted execution environments (TEEs) offered by current-generation microprocessors-in particular, Intel's SGX. To overcome the severe memory limitation of the TEEs, SkSES employs novel 'sketching' algorithms that maintain essential statistical information on genomic variants in input VCF files. By additionally incorporating efficient data compression and population stratification reduction methods, SkSES identifies the top k genomic variants in a cohort quickly, accurately and in a privacy-preserving manner.

A Noise-Aware Real-Time Processing Approach for Electroencephalogram Signal Classification

Electroencephalogram (EEG) signal processing has emerged as a critical problem for biometric applications due to its real-time requirement. While compressive sensing is an efficient method for signal compression, its application in EEG signal processing is limited due to its noise unawareness during transmission and time-consuming reconstruction procedure. In this paper, we propose a noise-aware sparse Bayesian learning approach with block structure (NA-BSBL) to achieve higher efficiency on data compression, reconstruction and classification. By applying novel structure for parameter and introducing the Mahalanobis Distance, our approach achieves an almost 20% reconstruction performance lift and 10% accuracy lift under noise condition. For further application of reconstructed EEG signal, we extract both the spatial and frequency domain features for classification. Experimental results show that the proposed approach can achieve 94% classification accuracy with 16% speed up compared with the conventional approach.

The XVC Video Code”A Revolutionary Software-Defined Video Compression Format

Efficient video compression is a key technology component that enables high-quality media services across different platforms and connection types. Several different video codecs have been used since the start of TV digitalization, and more efficient compression methods are constantly being developed. This paper focuses on mobile video streaming applications, a rapidly growing application area for which it is absolutely crucial to use the network resources as efficiently as possible. However, compression efficiency is not the only factor that determines which video codec is best suited to be used. There needs to be a clear and reasonable licensing scheme, the encoding complexity needs to be at a manageable level, but most importantly, the receivers must be able to support decoding of the codec. The xvc codec is a software-defined video compression format, which, in our tests, delivers unprecedented compression performance, is available with a single reasonable license, and with a light-weight decoding process that can be run in software on today’s mobile phones and tablets.

An efficient lossy cartoon image compression method

This paper introduces a new lossy approach for compression of cartoon images. The image is firstly partitioned into regions of roughly the same colour. The chain codes are then determined of all regions. The sequence of the obtained chain code symbols is transformed with the Burrows-Wheeler Transform, Move-To-Front transform, and compressed with Run-Length Encoding. In the final step, an arithmetic encoder may be used to compress the obtained binary stream additionally. The proposed algorithm is asymmetric, meaning that the decompression does not reverse all the steps of the compression procedure. The experimental results have shown that the described method produces considerably better compression ratios than JPEG, JPEG2000, WebP, SPIHT, PNG, and two of the algorithms specialised in compression of cartoon images: the algorithm using quad-tree, and RS-LZ algorithm.

Content-Based Light Field Image Compression Method With Gaussian Process Regression

Light field (LF) imaging enables new possibilities for digital imaging, such as digital refocusing, changing of focus plane, changing of viewpoint, scene-depth estimation, and 3D scene reconstruction, by capturing both spatial and angular information of light rays. However, one main problem in dealing with LF data is its sheer volume. In this context, efficient compression methods are needed for such a particular type of content. In this paper, we propose a content-based LF image-compression method with Gaussian process regression to improve the compression efficiency and accelerate the prediction procedure. First, the LF image is fed to the intra-frame codec of HEVC. In the prediction procedure, the prediction units (PUs) are classified as non-homogenous texture units, homogenous texture units, and visually flat units, based on the content property of the LF image. For each category, we design a corresponding Gaussian process regression (GPR)-based prediction method. Moreover, we propose a classification mechanism to exactly decide to which category the current PU belongs, so as to adjust the trade-off between the computational burden and the LF image coding efficiency. Experimental results demonstrate that the proposed LF image compression method is superior to several other state-of-the-art compression methods in terms of different quality metrics. Furthermore, the proposed method can also achieve a good visual quality of views rendered from decoded LF contents.

Data compression approach for the home energy management system

As a typical energy-cyber-physical system (e-CPS), home energy management system (HEMS) plays a critical role in power systems by accommodating higher levels of renewable generation, reducing power costs, and decreasing consumer energy bills. HEMS can help understand the home appliances energy use and learn the users’ preference so as to optimize home appliances operation and achieve higher energy efficiency. HEMS needs massive historical and real-time data for the above applications. Since HEMS is always based on a wireless sensor network, a more effective online data compression approach is necessary. The efficient data compression methods can not only relieve data transmission pressure and reduce data storage overhead, but also enhance data analysis efficiency. This paper proposes an online pattern-based data compression approach for the data generated by home appliances. The proposed approach first discovers the patterns of the time series data and then utilizes these patterns for the online data compression. The pattern discovery method in the proposed approach includes an online adaptive segmenting algorithm with incremental processing technique and a similarity metric based on piecewise statistic distance. The key issues of parameter selection and data reconstruction are also presented. Real-world common home appliance datasets are employed for comparing the performance of the proposed approach with those of six state-of-the-art algorithms. The experimental results demonstrate the outperformance of the proposed approach. Further complexity analysis shows that the proposed approach has linear time complexity. To the best of our knowledge, this is the first paper that performs online data compression based on the extracted patterns of the time series.

Performance Analysis of Turbo Iterative Decoder with EXIT Chart

The mobile communication industry around the globe is striving for new technologies as future wireless cellular systems are necessary to provide higher user data rates and offer smooth user access to the backbone communication network. The efficient data compression methods and error correcting codes are two of the most prominent approaches that ease applications in the overhead-mentioned systems and services. The major focus of this paper is on the performance analysis of turbo compressive codes using EXIT charts. The major advantages of the EXIT chart technique are the fact that they can be computed quickly, making it much more convenient than the traditionally simulated BER performance curves. Furthermore, the EXIT charts are a powerful technique which allows analyzing the constituent decoders in an iterative decoder separately.

Data compression for measured heterogeneous subsurface scattering via scattering profile blending

Subsurface scattering involves the complicated behavior of light beneath the surfaces of translucent objects that includes scattering and absorption inside the object’s volume. Physically accurate numerical representation of subsurface scattering requires a large number of parameters because of the complex nature of this phenomenon. The large amount of data restricts the use of the data on memory-limited devices such as video game consoles and mobile phones. To address this problem, this paper proposes an efficient data compression method for heterogeneous subsurface scattering. The key insight of this study is that heterogeneous materials often comprise a limited number of base materials, and the size of the subsurface scattering data can be significantly reduced by parameterizing only a few base materials. In the proposed compression method, we represent the scattering property of a base material using a function referred to as the base scattering profile. A small subset of the base materials is assigned to each surface position, and the local scattering property near the position is described using a linear combination of the base scattering profiles in the log scale. The proposed method reduces the data by a factor of approximately 30 compared to a state-of-the-art method, without significant loss of visual quality in the rendered graphics. In addition, the compressed data can also be used as bidirectional scattering surface reflectance distribution functions (BSSRDF) without incurring much computational overhead. These practical aspects of the proposed method also facilitate the use of higher-resolution BSSRDFs in devices with large memory capacity.

An efficient onboard compression method for multispectral images using distributed post-transform in the wavelet domain in conjunction with a fast spectral decorrelator

A remote sensing multispectral image compressor must be of low-complexity, high-robustness, and high-performance because it is usually located on a satellite platform where resources, such as power, memory, and processing capacity, are limited. Multispectral images having multiple bands are mainly compressed using compression algorithms based on three dimensional (3D) transforms, such as the 3D discrete wavelet transform, which exhibits satisfactory compression performance. However, the principal compression algorithm used for multispectral images having relatively a few bands is to encode each band independently, without considering the spectral redundancy between bands, which results in low compression performance. In this paper, an efficient compression method for multispectral images having a few bands is proposed, which is based on a distributed, improved post-transform in conjunction with a low-complexity, fast spectral decorrelator. First, a fast spectral transform and an improved post-transform having only a fast principal component analysis basis are used to generate the spectral and spatial sparse representation. Second, a distributed, improved bit plane encoding is integrated into the post-transform to remove the remaining spectral and spatial redundancy. Experimental results show that the proposed approach improves compression performance for test data in different performance measures: peak signal-to-noise ratio, mean structural similarity index, and visual information fidelity. Compared with current state-of-the-art compression techniques, the proposed method exhibits a performance improvement of 0.3–1.7 dB PSNR.

Light-field compression using a pair of steps and depth estimation.

Advanced handheld plenoptic cameras are being rapidly developed to capture information about light fields (LFs) from the 3D world. Rich LF data can be used to develop dense sub-aperture images (SAIs) that can provide a more immersive experience for users. Unlike conventional 2D images, 4D SAIs contain both the positional and directional information of light rays; the practical applications of handheld plenoptic cameras are limited by the huge volume of data required to capture this information. Therefore, an efficient LF compression method is vital for further application of the cameras. To this end, the pair of steps and depth estimation (PoS&DE) method is proposed in this paper, and the multiview video and depth (MVD) coding structure is used to relieve the LF coding burden. More specifically, a precise depth-estimation approach is presented for SAIs based on the cost function, and an SAI-guided depth optimization algorithm is designed to refine the initial depth map based on pixel variation tendency. Meanwhile, to reduce running time, intermediate SAI synthesis quality and coding bitrates, including the key SAIs selected and cost-computation steps, are set via extensive statistical experiments. In this way, only a limited number of optimally selected SAIs and their corresponding depth maps must be encoded. The experimental results demonstrate that our proposed LF compression solution using PoS&DE can obtain a satisfied coding performance.

An Efficient High-Rate Non-Binary LDPC Decoder Architecture With Early Termination

This paper presents a modified Trellis Min-Max (T-MM) algorithm together with the associated architecture for non-binary (NB) low-density parity-check (LDPC) decoders. The proposed T-MM algorithm is able to reduce the memory requirements for the check-node messages through an efficient compression method and enhance the error-rate performance using the appropriate decompression. A method of updating the a posteriori log-likelihood ratio in the delta domain is used to simplify the computational and storage complexity. In order to enhance the decoding throughput, a low-complexity early termination (ET) scheme is devised by using the hard decisions of the variable-to-check messages, where, although a minor overhead is introduced, there is no visible degradation in error rate. As a proof of concept, a row-parallel layered decoder for the 32-ary (837, 726) LDPC code is implemented using a 90-nm CMOS process. The proposed decoder achieves a throughput of 1.64 Gb/s at 526.32 MHz based on eight iterations and has an area of 6.86 mm 2 . When the ET scheme is enabled, the decoder achieves a maximum throughput of 4.68 Gb/s with a frame error rate of 3.25 x 10^-6 at E_b/N_0 = 4.5 dB. The proposed NB-LDPC decoder achieves the highest throughput and hardware efficiency compared to the state-of-the-art decoders, even when the ET scheme is not enabled.

Entropy-Based Selection of Cluster Representatives for Document Image Compression

In this work, we introduce an efficient method for lossy compression of digitalized documents. The method uses a dictionary which consists of class representatives defined using a minimum entropy criterion. The algorithm initially identifies the different symbols contained in a document image, and then the symbols are grouped in classes by means of a hierarchic clustering algorithm. For each class, a representative is selected using the principle of minimum entropy and suitable similarity distances. The technique creates a file in which every object belonging to a class is replaced by its class representative. Finally, the resulting file is compressed. The performance of the proposed algorithm is assessed using digitized files from a standard database for document compression along with different resolutions. Comparisons against other state-of-the-art algorithms are performed in this manuscript. The results establish quantitatively that the present methodology is a more efficient technique.

A Simple and Efficient IQ Data Compression Method Based on Latency, EVM, and Compression Ratio Analysis

Latency, error vector magnitude (EVM) and compression ratio (CR) are performance evaluation factors of IQ data compression. The purposes of IQ data compression are to minimize the latency, EVM and to maximize the CR. However, there is a tradeoff between them. In this paper, based on the evaluation factors, we analyze existing IQ data compression schemes and propose a simple and efficient method from the analyzed results. The proposed method is composed of partial cyclic prefix and cyclic suffix insertion, $K/L$ decimation, low-order bits removal, and partial Huffman code. It presents the techniques to minimize the EVM caused by $K/L$ decimation and to optimize the number of removable low bits and available Huffman encoding bits based on the average signal power and desired EVM. The proposed method can achieve the CR of 4 times or more in about 2% EVM for the LTE downlink and uplink. The simulation results show that the proposed method is applicable to SIMO and MIMO environments.

Efficient point cloud lossless data compression method based on an embedded Gray code structured light pattern sequence.

Despite the benefits derived from the development of 3D techniques to improve the acceleration and accuracy of 3D scanning operations over the past two decades and its wide range applications in various industries (e.g., quality control and inspection, reverse engineering, robotics), there are restrictions on data transfer, data storage, and even the development of real-time scanning methods due to the enlarging data size (point cloud). According to the importance of maintaining all the output data of the scanner in instrumentation engineering, the need to apply minimum loss or lossless data compression algorithms is more than ever evident. In this regard, this paper presents a novel method in point cloud lossless data compression, using a Gray code structured light pattern sequence and image-based compression. The empirical evaluation and results of the proposed method demonstrate that this idea is reliable and practical to achieve a distinct compression ratio among other lossless point cloud compression methods.

An efficient method for specular-enhanced BTF compression

The Bidirectional Texture Function (BTF) has been used in the data-driven model to recover the reflectance of complex surfaces. The high-dimensionality of the BTF causes huge measurement data, but that needs to be compressed when real-time rendering is required. However, the distribution of BTF data is highly non-uniform due to non-lambertian reflections. Projecting the measured data onto a linear space constructed by a statistical analysis such as PCA results in low-quality of data compression. In this paper, we propose a computationally efficient and robust data compression method. Based on the assumption that there exist many overlapping measurements, we seek to find reflectance holes caused by the non-lambertian reflection with a series of linear algorithms. Before performing the standard compression, our method separates diffuse reflections which can be well approximated with a linear model and compress them using a standard factorization. Specular components detected in the reflectance hole are separately stored using a sparse matrix representation to prevent the loss of the range of BTF data. Experimental results show that our method effectively improves the compression accuracy as well as the visual quality with a competitive computation time.

An Efficient Image Compression Method by Using Optimized Discrete Wavelet Transform and Huffman Encoder

An Efficient Image Compression Method by Using Optimized Discrete Wavelet Transform and Huffman Encoder

A lossless compression method incorporating sensor fault detection for underwater acoustic sensor array

This article proposes an efficient lossless compression method for an underwater acoustic sensor array. The proposed method first decides whether the input signal is coming from a normal or a fault...