The Cosmos and Crystal Connection: What We Can Learn About Data Compression

Image compression is the application of data compression techniques to two-dimensional digital images I(x, y) , to reduce the redundancy of the image data for storage or transmission in an efficient form. Image compression can be classified into two categories: lossless or lossy. Lossless compression, which achieves smaller compression ratios than lossy compression, mainly takes advantage of the image contents containing a non uniform probability distribution for a variable-length representation of the image pixels. Such images include technical drawings, icons or comics, and high-value contents such as medical imagery or image scans made for archival purposes. However, lossy compression methods, especially when they achieve a very high compression ratio, can introduce compression artifacts. Nevertheless, lossy compressions are especially suitable for natural images, such as photos, in applications where a minor (sometimes imperceptible) loss of fidelity is acceptable when it is desirable to achieve a substantial reduction in bitrate. Most of the stateof-the-art image compression standards use a combination of lossy and lossless algorithms to achieve the best performance. The Joint Photographic Experts Group (JPEG) [1], a discrete cosine transform(DCT)-based technique, is the most widely used standardized lossy image compression mechanism; it was designed for compressing either full-color or gray-scale images of natural, real-world, scenes. It works well for photographs, naturalistic artwork and similar material if the compression ratio is about 20 : 1, which is much better than the 4 : 1 compression ratio provided by a lossless compression method such as the Graphics Interchange Format (GIF) [2]. However, it does not work so well for lettering, simple cartoons, or line drawings.

LAPAN-A3/IPB satellite is the latest Indonesian experimental microsatellite with remote sensing and earth surveillance missions. The satellite has three optical payloads, which are multispectral push-broom imager, digital matrix camera and video camera. To increase data transmission efficiency, the multispectral imager data can be compressed using either lossy or lossless compression method. This paper aims to analyze Differential Pulse Code Modulation (DPCM) method and Huffman coding that are used in LAPAN-IPB satellite image lossless compression. Based on several simulation and analysis that have been done, current LAPAN-IPB lossless compression algorithm has moderate performance. There are several aspects that can be improved from current configuration, which are the type of DPCM code used, the type of Huffman entropy-coding scheme, and the use of sub-image compression method. The key result of this research shows that at least two neighboring pixels should be used for DPCM calculation to increase compression performance. Meanwhile, varying Huffman tables with sub-image approach could also increase the performance if on-board computer can support for more complicated algorithm. These results can be used as references in designing Payload Data Handling System (PDHS) for an upcoming LAPAN-A4 satellite.

WITHDRAWN: Implementing lossless compression during image processing by integrated approach

The study of diagnostic accuracy of chest nodules by using different compression methods

Exciting opportunities for early science at the National Synchrotron Light Source II (NSLS-II) drew over 260 participants to a workshop at Brookhaven National Laboratory, August 12–13, 2013. NSLS users planning to continue their work at the new facility joined prospective NSLS-II users to hear talks and debate possible research projects, beginning the process of creating a pool of experiment proposals and forming research teams.

The project focuses on low temperature geochemistry applications of X-ray fluorescence microprobes primarily at the National Synchrotron Light Source (NSLS-I) but also at National Synchrotron Light Source II (NSLS-II), Brookhaven National Laboratory (BNL), Upton, NY. The microprobes can be used to determine the composition, structure, oxidation state, and bonding characteristics of earth materials with trace element sensitivity and micrometer spatial resolution. Investigators on this project are members of the Center for Advanced Radiation Sources (CARS) at the University of Chicago, a team that developed and managed the highly-successful microprobe beamline X26A at the National Synchrotron Light Source I (NSLS-I) at BNL from the 1980s untilthe closing of NSLS-I in September 2014. The closing of NSLS-I coincided with the start-up of the newly-constructed, 3rd-generation synchrotron NSLS-II, also at BNL. During the course of this project, a subaward was let to the University of Kentucky to partially support the efforts of a technician at the beamlines.

: Li- and Mn-rich cathode materials have been considered as promising candidates for application of lithium-ion batteries in vehicles due to their high capacity and high energy density (capacity around 250 mAh/g). However, these materials suffer from a serious problem of voltage fade. The average discharge voltage keeps decreasing as the cycling goes on and results in decreased energy output after limited cycles. In order to solve this problem, many efforts have been put in to understand the origin of such phenomenon, mostly from structural and chemical points of view. In this work, the important roles of crystallinity, or the microstructure of the cathode material in voltage fade are studied. The study is carried out on Li2Ru0.5Mn0.5O3 as an example. The material is electrochemically prelithiated to around Li3.2Ru0.5Mn0.5O3 first and then delithiated to Li2Ru0.5Mn0.5O3 . It is shown that even though the crystal structure mostly remains the same, the crystallinity is severely damaged by breaking the sub-micro domains into very fine nano-sized domains. Such loss of crystallinity causes much more significant voltage fade in the following cycling compared with those normally cycled samples. The more severe voltage fade caused by low crystallinity can be attributed to the accelerated oxygen loss due to the increased defects and surface area of the prelithiated sample. These results indicate the great importance of crystallinity of the cathode materials. Acknowledgement The work at Brookhaven National Laboratory was supported by the U.S. Department of Energy, the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Vehicle Technologies under Contract No. DE-SC0012704. Use of the National Synchrotron Light Source II (NSLS-II) and Center for Functional Nanomaterials at Brookhaven National Laboratory were supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contracts No. DE-SC0012704.

The increasing test data volume is considered as a biggest challenge in circuit under test. This challenge leads to higher computational complexity associated with the testing circuits. To reduce the testing time and addresses the problem related to high overhead, high fault coverage andincreased power dissipationmethod is used in system-on-chip (SoC). A new test compression method called divide and compress (D&C) lossless compression coder with Discrete Cosine Transformis used to enhance the compression capability. This framework of D&C lossless compression coder combines the lossless and lossy compression methods. The Discrete Cosine Transform (DCT) manipulates each data bit inside file and this reduces the file size without any data loss after decoding to lossless compression.Depending on the data characteristic, the compression of data is done without any loss in data. Further, the decompression is attained using advanced Finite State Machine (FSM) to reduce overhead. The experimental results on large benchmark circuits like ITC’99 and ISCAS’99 proves that the proposed D&C lossless compression attains reduced testing time and better compression ratio. Further, itlays only soft burden on the hardware.

Learning-based point cloud compression methods have made significant progress in terms of performance. However, these methods still encounter challenges including high complexity, limited compression modes, and a lack of support for variable rate, which restrict the practical application of these methods. In order to promote the development of practical point cloud compression, we propose an efficient unified point cloud geometry compression framework, dubbed as UniPCGC. It is a lightweight framework that supports lossy compression, lossless compression, variable rate and variable complexity. First, we introduce the Uneven 8-Stage Lossless Coder (UELC) in the lossless mode, which allocates more computational complexity to groups with higher coding difficulty, and merges groups with lower coding difficulty. Second, Variable Rate and Complexity Module (VRCM) is achieved in the lossy mode through joint adoption of a rate modulation module and dynamic sparse convolution. Finally, through the dynamic combination of UELC and VRCM, we achieve lossy compression, lossless compression, variable rate and complexity within a unified framework. Compared to the previous state-of-the-art method, our method achieves a compression ratio (CR) gain of 8.1% on lossless compression, and a Bjontegaard Delta Rate (BD-Rate) gain of 14.02% on lossy compression, while also supporting variable rate and variable complexity.

When detecting a target, the radar receives a large amount of complex data. Through three-dimensional imaging, the large amount of data can be converted into relevant features of the scattering center for storage, thereby realizing data compression. This paper proposes a radar data compression method based on scattering center extraction. In this paper, a mathematical model is established, and the scattering center is extracted by the three-dimensional ESPRIT imaging method, and the number of scattering centers is estimated. A sweep frequency reconstruction algorithm is used on the compressed data to verify the recoverability of the compressed data. By using the method of this paper, a higher compression ratio can be obtained while ensuring smaller data errors. Experimental data processing results validate the conclusions of this paper.

With advancement of technology, the image processing techniques play an important role. Image processing is required to improve the pictorial information for human perception and for autonomous machine applications. It finds application in various fields such as quality control, remote sensing, imaging science, etc. It also finds application in areas where efficient storage and transmission of images is necessary, Say for example; we want to store an image in the computer with less disk space. Also, in cases where we want to signal an image or video on a transmission medium with less bandwidth communication channel. Images are usually in the form of matrices and an uncompressed image uses huge number of bytes for storage. Image compression is used to reduce the size of the bitmap without compromising on the quality of an image. This enables to store large number of images in the same given amount of storage space. Image compression will also reduce the time taken to transmit or to download images from the internet. Many methods are available to compress an image file. The image compression methods which have gained popularity are the transform based coding methods like Discrete Cosine Transform (DCT), discrete wavelet Transform (DWT) and fractals. However these methods have drawbacks like low compression ratio and high encoding time. In this paper a new hybrid methodology is proposed by combining lossy and lossless compression methods. The proposed hybrid technique combines DCT and fractal quadtree decomposition with Huffman encoding of threshold value 0.2. The results obtained from the proposed method are then compared with state-of-the-art compression methods.

Background and objectiveShort-read sequencing is becoming the standard of practice for the study of structural variants associated with disease. However, with the growth of sequence data largely surpassing reasonable storage capability, the biomedical community is challenged with the management, transfer, archiving, and storage of sequence data.MethodsWe developed Hierarchical mUlti-reference Genome cOmpression (HUGO), a novel compression algorithm for aligned reads in the sorted Sequence Alignment/Map (SAM) format. We first aligned short reads against a reference genome and stored exactly mapped reads for compression. For the inexact mapped or unmapped reads, we realigned them against different reference genomes using an adaptive scheme by gradually shortening the read length. Regarding the base quality value, we offer lossy and lossless compression mechanisms. The lossy compression mechanism for the base quality values uses k-means clustering, where a user can adjust the balance between decompression quality and compression rate. The lossless compression can be produced by setting k (the number of clusters) to the number of different quality values.ResultsThe proposed method produced a compression ratio in the range 0.5–0.65, which corresponds to 35–50% storage savings based on experimental datasets. The proposed approach achieved 15% more storage savings over CRAM and comparable compression ratio with Samcomp (CRAM and Samcomp are two of the state-of-the-art genome compression algorithms). The software is freely available at https://sourceforge.net/projects/hierachicaldnac/with a General Public License (GPL) license.LimitationOur method requires having different reference genomes and prolongs the execution time for additional alignments.ConclusionsThe proposed multi-reference-based compression algorithm for aligned reads outperforms existing single-reference based algorithms.

With the rapid development of automated terminals, it has greatly contributed to the rapid economic growth. However, the rapid development of terminals has led to a rapid increase in the data that needs to be stored. Storing all the massive terminal data without compression requires a huge amount of storage space, while online transmission consumes a large amount of network bandwidth and puts a lot of pressure on the data transmission lines. In practice, the collected sequence data are not always variable and valuable, and may contain a lot of redundant data, which is detrimental to the analysis efficiency and storage space of the data. Meanwhile, for data with different attributes, the compression methods adopted should be different. To address such issues, this paper proposes a fusion lossy and lossless data compression method based on signal attributes, where the lossy compression algorithm adopts compression perception and the lossless compression algorithm adopts LZW coding. For important data, only the lossless compression method is adopted to prevent the loss of important information; for general data, the fusion lossy and lossless compression method is adopted to improve the compression rate.

Wavelets are defined as mathematical functions that segment the data into different frequency levels. We can easily capture the fine and coarse details of an image or signal referred to as a subband. And it also helps in subband thresholding to achieve good compression performance. In recent days in telemedicine services, the handling of medical images is prominently increasing and it leads to the demand for medical image compression. While compressing the medical images, we have to concentrate on the data that holds important information, and at the same time, it must retain the image quality. Near-Lossless compression plays an essential role to achieve a better compression ratio than lossy compression and provides better quality than lossless compression. In this paper, we analyzed the sub-banding of Discrete Wavelet Transform (DWT) using different types of wavelets and made an optimal selection of wavelets for subband thresholding to attain a good compression performance with an application to medical images. We used Set Partitioning In Hierarchical Trees (SPIHT) compression scheme to test the compression performance of different wavelets. The Peak Signal to Noise Ratio (PSNR), Bits Per Pixel (BPP), Compression Ratio, and percentage of number of zeros are used as metrics to assess the performance of all the selected wavelets. And to find out its efficiency in possessing the essential information of medical images, the subband of the selected wavelets is further utilized to devise the near-lossless compression scheme for medical images.

The problems of measurement signal compression recorded under long-term monitoring conditions are presented in the paper. The functional quality of lossy compression algorithms in metrological applications depends strongly on the data stream dynamic properties. This was the reason for presenting a proposal to use synthetic test signals for investigating and classification compression algorithms. Application of such signals to study the lossy compression algorithms makes the results obtained this way independent of the shape of an individual signal and makes them obligatory for the whole class of measurement signals. By changing parameters of synthetic signals, the functioning of algorithms can be investigated within certain intervals of parameter values, and not, as it is for real signals, for single values of these parameters. A method for generating synthetic test signals for testing metrological properties of lossy compression methods allowing unification of the obtained results is presented in the paper. The results of exemplary simulation experiments are presented.