Improving Lossy Compression for SZ by Exploring the Best-Fit Lossless Compression Techniques

Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as images—particularly bilevel ones, or ones arising in medical and remote-sensing applications, or ones that may be required to be certified true for legal reasons. Moreover, during the process of lossy compression, many occasions for lossless compression of coefficients or other information arise. This paper surveys techniques for lossless compression. The process of compression can be broken down into modeling and coding. We provide an extensive discussion of coding techniques, and then introduce methods of modeling that are appropriate for text and images. Standard methods used in popular utilities (in the case of text) and international standards (in the case of images) are described.

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Learning Better Lossless Compression Using Lossy Compression

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ◽

10.1109/cvpr42600.2020.00667 ◽

2020 ◽

Cited By ~ 1

Author(s):

Fabian Mentzer ◽

Luc Van Gool ◽

Michael Tschannen

Keyword(s):

Lossless Compression ◽

Lossy Compression

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A Fast Method of Visually Lossless Compression of Dental Images

Applied Sciences ◽

10.3390/app11010135 ◽

2020 ◽

Vol 11 (1) ◽

pp. 135

Author(s):

Sergey Krivenko ◽

Vladimir Lukin ◽

Olha Krylova ◽

Liudmyla Kryvenko ◽

Karen Egiazarian

Keyword(s):

Signal To Noise Ratio ◽

Lossless Compression ◽

Lossy Compression ◽

Fast Method ◽

Signal To Noise ◽

Quantization Step ◽

Partition Scheme ◽

Compressed Images ◽

Image Coder ◽

Noise Ratio

A noniterative approach to the problem of visually lossless compression of dental images is proposed for an image coder based on the discrete cosine transform (DCT) and partition scheme optimization. This approach considers the following peculiarities of the problem. It is necessary to carry out lossy compression of dental images to achieve large compression ratios (CRs). Since dental images are viewed and analyzed by specialists, it is important to preserve useful diagnostic information preventing appearance of any visible artifacts due to lossy compression. At last, dental images may contain noise having complex statistical and spectral properties. In this paper, we have analyzed and utilized dependences of three quality metrics (Peak signal-to-noise ratio, PSNR; eak Signal-to-Noise Ratio using Human Visual System and Masking (PSNR-HVS-M); and feature similarity, FSIM) on the quantization step (QS), which controls a compression ratio for the so-called advanced DCT coder (ADCTC). The threshold values of distortion visibility for these metrics have been considered. Finally, the recent results on detectable changes in noise intensity have been incorporated in the QS setting. A visual comparison of original and compressed images allows to conclude that the introduced distortions are practically undetectable for the proposed approach; meanwhile, the provided CR lies within the interval.

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PENERAPAN DISCRETE COSINE TRANSFORM (DCT) TERHADAP KOMPRESI CITRA DIGITAL

Indonesian Journal of Business Intelligence (IJUBI) ◽

10.21927/ijubi.v4i1.1790 ◽

2021 ◽

Vol 4 (1) ◽

pp. 31

Author(s):

Bayu Dwi Raharja

Keyword(s):

Image Compression ◽

Discrete Cosine Transform ◽

Data Exchange ◽

Data Transfer ◽

Lossless Compression ◽

Lossy Compression ◽

Image Size ◽

Image Information ◽

Cosine Transform ◽

Human Eye

<p>Image compression is a process that can reduce image size. In general, there are two types, lossless compression and loss compression. The method usually used in the JPEG standard is lossy compression, which removes some of the image information and takes advantage of the weakness in the insensitivity of the human eye in recognizing color gradations. With such large storage, data transfer or exchange is also increasingly difficult, especially data exchange using small storage areas. Tools that usually limit the size of storage for exchanging data. From the existing results using the DCT method is able to compress files up to 96%. Can be seen from the overall file average up to 74%</p>

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ANALISIS PERBANDINGAN ALGORITMA RIVEST SHAMIR ADLEMAN (RSA) & RIVEST CODE 6 (RC6) DALAM KEAMANAN CITRA DIGITAL

Jurnal Media Elektro ◽

10.35508/jme.v8i1.1378 ◽

2019 ◽

pp. 17-24

Author(s):

Syahrul Syahrul ◽

Silvester Tena ◽

Sarlince O. Manu

Keyword(s):

Lossless Compression ◽

Lossy Compression

Citra digital merupakan satu media yang dapat disimpan pada media penyimpanan atau ditransmisikan melalui jaringan. Namun dalam transmisi dapat terjadi tindakan pencurian dan penyalahgunaan data yang merugikan pihak berkepentingan terhadap data tersebut. Untuk melindungi dan menjaga kerahasiaan sebuah citra digital digunakan metode kriptografi. Pada penelitian ini dibandingkan dua algoritma yaitu Rivest Shamir Adleman (RSA) dan Rivest Code 6 (RC6). RSA merupakan salah satu algoritma asimetris dimana kunci enkripsi dan dekripsi yang digunakan berbeda, sedangkan RC6 merupakan algoritma simetris dimana kunci yang digunakan sama. Berdasarkan pengujian,waktu operasi algoritma RC6 lebih cepat dari algoritma RSA. Untuk pengujian noise yang dilakukan RSA lebih dapat bertahan daripada RC6. Hasil pengujian RSA dapat bertahan pada noise gaussian dengan nilai mean 0,00001 dan varian 0,000001 mendapatkan nilai MSE 1252,98 dan PSNR 43,85 dB, sedangkan RC6 tidak dapat bertahan dengan noise ini. Kemudian untuk noise salt & pepper, RSA dapat bertahan pada nilai density 0,05 dengan nilai MSE 1256,66 dan PSNR 42,71 dB, sedangkan RC6 dapat bertahan pada nilai density 0,01 dengan nilai MSE 1108,85 dan PSNR 40,72 dB. Berdasarkan pengujian kompresi kedua algoritma ini sama-sama tidak tahan terhadap lossy compression, sedangkan untuk lossless compression kedua algoritma masih bisa bertahan yakni hasil dekripsi sama dengan citra asli.

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FCLQC: fast and concurrent lossless quality scores compressor

BMC Bioinformatics ◽

10.1186/s12859-021-04516-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Minhyeok Cho ◽

Albert No

Keyword(s):

Compression Ratio ◽

Random Access ◽

Lossless Compression ◽

Lossy Compression ◽

General Purpose ◽

Quality Score ◽

Compression Rate ◽

Sequencing Data ◽

Compression Algorithms ◽

Compression Speed

Abstract Background Advances in sequencing technology have drastically reduced sequencing costs. As a result, the amount of sequencing data increases explosively. Since FASTQ files (standard sequencing data formats) are huge, there is a need for efficient compression of FASTQ files, especially quality scores. Several quality scores compression algorithms are recently proposed, mainly focused on lossy compression to boost the compression rate further. However, for clinical applications and archiving purposes, lossy compression cannot replace lossless compression. One of the main challenges for lossless compression is time complexity, where it takes thousands of seconds to compress a 1 GB file. Also, there are desired features for compression algorithms, such as random access. Therefore, there is a need for a fast lossless compressor with a reasonable compression rate and random access functionality. Results This paper proposes a Fast and Concurrent Lossless Quality scores Compressor (FCLQC) that supports random access and achieves a lower running time based on concurrent programming. Experimental results reveal that FCLQC is significantly faster than the baseline compressors on compression and decompression at the expense of compression ratio. Compared to LCQS (baseline quality score compression algorithm), FCLQC shows at least 31x compression speed improvement in all settings, where a performance degradation in compression ratio is up to 13.58% (8.26% on average). Compared to general-purpose compressors (such as 7-zip), FCLQC shows 3x faster compression speed while having better compression ratios, at least 2.08% (4.69% on average). Moreover, the speed of random access decompression also outperforms the others. The concurrency of FCLQC is implemented using Rust; the performance gain increases near-linearly with the number of threads. Conclusion The superiority of compression and decompression speed makes FCLQC a practical lossless quality score compressor candidate for speed-sensitive applications of DNA sequencing data. FCLQC is available at https://github.com/Minhyeok01/FCLQC and is freely available for non-commercial usage.

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IMPLEMENTASI KOMPRESI CITRA DIGITAL DENGAN MENGATUR KUALITAS CITRA DIGITAL

Jurnal Ilmiah SINUS ◽

10.30646/sinus.v16i2.363 ◽

2018 ◽

Vol 16 (2) ◽

Author(s):

Bayu Dwi Raharja ◽

Paulus Harsadi

Keyword(s):

Image Compression ◽

Information Exchange ◽

Lossless Compression ◽

Lossy Compression ◽

Popular Media ◽

Original Image ◽

Missing Information ◽

Compressed Images ◽

Storage Media ◽

Large Size

Image is one of the most popular media used in today’s information exchange. Increased needs for the use of images must also be supported by available storage media. The imagery generated from each high-resolution camera device has a relatively large size. Image compression is a data compression application performed on digital image in order to reduce the redundancy of the data contained in the image so that it can be stored or transmitted efficiently.Image compression techniques can be grouped into two namely lossless compression and lossy compression. Lossy compression is a method to compress the image where compressed image decompression results are not the same as the original image because there is missing information, but still can be tolerated by the eye. Lossless compression is an image compression where the decompression of compressed images is the same as the original image, no information is lost. For the following image compression by reducing image quality that can still be tolerated by the eye.

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mspack: efficient lossless and lossy mass spectrometry data compression

Bioinformatics ◽

10.1093/bioinformatics/btab636 ◽

2021 ◽

Author(s):

Felix Hanau ◽

Hannes Röst ◽

Idoia Ochoa

Keyword(s):

Mass Spectrometry ◽

Lossless Compression ◽

Lossy Compression ◽

General Purpose ◽

Mass Spectrometry Data ◽

Supplementary Information ◽

Compression Algorithms ◽

Single File ◽

Comparable Accuracy ◽

Better Than

Abstract Motivation Mass spectrometry data, used for proteomics and metabolomics analyses, have seen considerable growth in the last years. Aiming at reducing the associated storage costs, dedicated compression algorithms for Mass Spectrometry (MS) data have been proposed, such as MassComp and MSNumpress. However, these algorithms focus on either lossless or lossy compression, respectively, and do not exploit the additional redundancy existing across scans contained in a single file. We introduce mspack, a compression algorithm for MS data that exploits this additional redundancy and that supports both lossless and lossy compression, as well as the mzML and the legacy mzXML formats. mspack applies several preprocessing lossless transforms and optional lossy transforms with a configurable error, followed by the general purpose compressors gzip or bsc to achieve a higher compression ratio. Results We tested mspack on several datasets generated by commonly used mass spectrometry instruments. When used with the bsc compression backend, mspack achieves on average 76% smaller file sizes for lossless compression and 94% smaller file sizes for lossy compression, as compared to the original files. Lossless mspack achieves 10 - 60% lower file sizes than MassComp, and lossy mspack compresses 36 - 60% better than the lossy MSNumpress, for the same error, while exhibiting comparable accuracy and running time. Availability mspack is implemented in C ++ and freely available at https://github.com/fhanau/mspack under the Apache license. Supplementary information Supplementary data are available at Bioinformatics online.

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Question Wording Effects in the Assessment of Global Self-Esteem

European Journal of Psychological Assessment ◽

10.1027//1015-5759.16.1.13 ◽

2000 ◽

Vol 16 (1) ◽

pp. 13-19 ◽

Cited By ~ 31

Author(s):

Martin Dunbar ◽

Graeme Ford ◽

Kate Hunt ◽

Geoff Der

Keyword(s):

Reading Ability ◽

Factor Model ◽

Self Esteem ◽

Reading Scores ◽

Poor Reading ◽

Method Effect ◽

Two Samples ◽

Positive Item ◽

Method Effects ◽

Best Fit

Summary: Marsh (1996) produced evidence that method effects associated with negatively worded items might be responsible for the results of earlier factor analytic studies that reported finding positive and negative self-esteem factors in the Rosenberg Global self-esteem scale ( Rosenberg, 1965 ). He analyzed data collected from children using a 7-item self-esteem measure. This report details attempts to replicate Marsh 's analysis in data collected from two samples of adults who completed the full 10-item Global Self-Esteem (GSE) scale. The results reported here are similar to those given by Marsh in so much as a correlated uniquenesses model produced a superior fit to the data than the simple one factor model (without correlated uniquenesses) or the often reported two factor (positive and negative self-esteem) model. However, whilst Marsh reported that the best fit was produced by allowing negative item uniquenesses to correlate with each other, the model that produced the best fit to these data was one that contained correlated positive item uniquenesses. Supporting his claim that differential responding to negative and positive self-esteem items reflects a method effect associated with reading ability, Marsh also showed that factors associated with negative and positive items were most distinct among children who had poor reading scores. We report a similar effect among a sample of older adults where the correlation between these factors was compared across two groups who were selected according to their scores on a test of verbal reasoning.

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Direct Assessment of Effective Renal Plasma Flow from Renoscintigraphy with a Gamma Camera and an On-Line Computer

Nuklearmedizin ◽

10.1055/s-0037-1620658 ◽

1981 ◽

Vol 20 (06) ◽

pp. 274-278

Author(s):

J. Liniecki ◽

J. Bialobrzeski ◽

Ewa Mlodkowska ◽

M. J. Surma

Keyword(s):

Count Rate ◽

Renal Plasma Flow ◽

Early Period ◽

Least Square Method ◽

Least Square ◽

Uptake Coefficient ◽

Positive Linear Correlation ◽

On Line ◽

Best Fit ◽

Plasma Activity

A concept of a kidney uptake coefficient (UC) of 131I-o-hippurate was developed by analogy from the corresponding kidney clearance of blood plasma in the early period after injection of the hippurate. The UC for each kidney was defined as the count-rate over its ROI at a time shorter than the peak in the renoscintigraphic curve divided by the integral of the count-rate curve over the "blood"-ROI. A procedure for normalization of both curves against each other was also developed. The total kidney clearance of the hippurate was determined from the function of plasma activity concentration vs. time after a single injection; the determinations were made at 5, 10, 15, 20, 30, 45, 60, 75 and 90 min after intravenous administration of 131I-o-hippurate and the best-fit curve was obtained by means of the least-square method. When the UC was related to the absolute value of the clearance a positive linear correlation was found (r = 0.922, ρ > 0.99). Using this regression equation the clearance could be estimated in reverse from the uptake coefficient calculated solely on the basis of the renoscintigraphic curves without blood sampling. The errors of the estimate are compatible with the requirement of a fast appraisal of renal function for purposes of clinical diagknosis.

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