Dual-Direction Prediction Vector Quantization for Lossless Compression of LASIS Data

An adaptive VQ method used on interferential multi-spectral image lossless compression

In this paper, we propose a new low bit rate scheme that combines the Discrete Wavelet transform (IWT) and Vector Quantization (VQ) for image compression with high Compression Ratio (CR) in order to achieve acceptable quality of output images, Image compression using the combination of DWT willy VQ has been considered in many recent works; here in our paper the DWT was performed on several gray scale standard images, each of (256X256) byte. We used 4 images as the training images of the train VO using LBG algorithm and got the final optimal codebook. Then we used another two images to compare our output image with JPEG 2000 standard winch based on Wavelet and Scalar quantization. Our proposed system used a VO codebook with Variable cod hook size and dimensions were used to compress wavelet Transform) coefficients resulting using Danny wavelet types. Our pro system is slowed so that the best Wavelet types that you can use for compression is COIF 2. Our proposed system gave better PSNR than G2000 standard at high (CR), above than (5L), and this achieved a VQ codebook size (1024 -512) with dimension equal to 256. In the paper a typical system for the application of image compression system and new scientific results are present.

The Geostationary Imaging Fourier Transform Spectrometer (GIFTS), as part of NASA's New Millennium Program, is an advanced instrument to provide high-temporal-resolution measurements of atmospheric temperature and water vapor, which will greatly facilitate the detection of rapid atmospheric changes associated with destructive weather events, including tornadoes, severe thunderstorms, flash floods, and hurricanes. The Committee on Earth Science and Applications from Space under the National Academy of Sciences recommended that NASA and NOAA complete the fabrication, testing, and space qualification of the GIFTS instrument and that they support the international effort to launch GIFTS by 2008. Lossless data compression is critical for the overall success of the GIFTS experiment, or any other very high data rate experiment where the data is to be disseminated to the user community in real-time and archived for scientific studies and climate assessment. In general, lossless data compression is needed for high data rate hyperspectral sounding instruments such as GIFTS for (1) transmitting the data down to the ground within the bandwidth capabilities of the satellite transmitter and ground station receiving system, (2) compressing the data at the ground station for distribution to the user community (as is traditionally performed with GOES data via satellite rebroadcast), and (3) archival of the data without loss of any information content so that it can be used in scientific studies and climate assessment for many years after the date of the measurements. In this paper we study lossless compression of GIFTS data that has been collected as part of the calibration or ground based tests that were conducted in 2006. The predictive partitioned vector quantization (PPVQ) is investigated for higher lossless compression performance. PPVQ consists of linear prediction, channel partitioning and vector quantization. It yields an average compression ratio of 4.65 on the GIFTS test data, which significantly outperforms the standard compression methods such as JPEG-2000, JPEG-LS, and CCSDS IDC 9/7M & 5/3.

This paper conducts a comparative analysis of the impact of unsparsing by data scaling on lossless data compression methods. The most commonly used data compression algorithms such as gzip, zlib, bzip2, and lzma are tested by their compression efficiency in unsparsed datasets by the performance metrics such as compression time, decompression time, and compression ratio for both the original and scaled datasets. Five different data scaling techniques are investigated to transform the original data into unsparsed data such as min-max, robust, absolute max, standardization, and normalization. Our research reveals relationships between data scaling methodologies and compression performance, with disparities in compression efficiency and computational complexity. Fur-thermore, we investigate the effects of scaling on compression ratio and provide results for improving the understanding of the factors that influence lossless data compression methods for non-sparse tabular data.

The authors point out that the LBG algorithm (see Y. Linde et al., (1980)) requires a lot of computation as the training vectors increase, and proposes a fast VQ (vector quantization) algorithm for a large amount of training data. This algorithm consists of three steps: first, divide training vectors into small groups; second, quantize each group into a few codewords by the LBG algorithm; finally, construct a codebook by clustering these codewords using the LBG algorithm again. The authors also report they can reduce the distortion error of the algorithm by adapting an effective data-dividing method. In experiments of quantizing 17500 training vectors into 512 codewords, this algorithm requires only 1/6 computation time compared with the conventional algorithm, while the increase of distortion is only 0.5 dB.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The compression of three-dimensional ultraspectral sounder data is a challenging task given its unprecedented size. We develop a fast precomputed vector quantization (FPVQ) scheme with optimal bit allocation for lossless compression of ultraspectral sounder data. The scheme consists of linear prediction, bit-depth partitioning, vector quantization, and optimal bit allocation. Linear prediction serves as a whitening tool to make the prediction residuals of each channel close to a Gaussian distribution, and then these residuals are partitioned based on bit depths. Each partition is further divided into several sub-partitions with various 2/sup k/ channels for vector quantization. Only the codebooks with 2/sup m/ codewords for 2/sup k/-dimensional normalized Gaussian distributions are precomputed. A new algorithm is developed for optimal bit allocation among subpartitions. Unlike previous algorithms that may yield a sub-optimal solution, the proposed algorithm guarantees to find the minimum of the cost function under the constraint of a given total bit rate. Numerical experiments upon the NASA AIRS data show that the FPVQ scheme gives high compression ratios for lossless compression of ultraspectral sounder data.

Image data compression is one of the most important techniques in digital image processing that used to reduce the size of image data with taking into account getting good quality of the compressed image, before several decades the researchers produced a lot of coding methods to decrease the image size, these methods include loss data compression and lossless data compression. in this research vector quantization techniques have been used to compress color image, firstly vector quantization has been applied using only cosine transform and then using principle component transform together with cosine transform, secondly vector quantization (VQ) has been achieved using wavelet transform only and then using the same transform with the principle component transform, many statistical properties have been shown and many fidelity criteria have been computed. This manuscript also includes the theory concepts of Vector Quantization, cosine transformation, principle component analysis and wavelet transform.

Codebook design plays an important role in the performance of signal processing based on vector quantization (VQ) just as speech coding, data compression and pattern recognition. LBG is one of the most effective algorithms and is widely used in codebook generation. Some problems still exist while its performance is remarkable. The LBG algorithm is easy to fall into local optimum. Usually there is strong correlation between the best solution and the initial selection for codebook design. It means that the quantization performance of codebooks from the same training data may varies in a certain range. There is also a certain probability for the algorithm to generate empty voronoi cell. In order to solve these problems, a novel algorithm based on ant colony clustering algorithm and genetic algorithm is proposed in this paper. The new algorithm takes advantage of the excellent global optimal searching ability of genetic algorithm. At the same time, the ant colony clustering algorithm is combined into the process. The dynamic change of the searching direction is adopted during crossover stage. The simulation results of line spectrum frequency parameters in mixed linear excitation prediction (MELP) show that the proposed algorithm is more efficient in its quantization performance compared to that of the LBG and genetic algorithms. Meanwhile, it has good stability in quantization performance. Index Terms - Codebook design, Vector quantization, Genetic algorithm, Ant colony clustering algorithm.

The partitioned predictive vector quantization (PPVQ) algorithm is known for its high compression ratio for lossless compression of the ultraspectral sounder data with high spatial and spectral resolutions. With the advent of the multicore technologies, parallelization of several parts of the algorithm has been explored in previous work using a compute unified device architecture (CUDA) aided environment on the Graphics Processing Unit (GPU). Recently the Intel Many Integrated Core (MIC) architecture on a coprocessor is introduced which shows promise in handling more divergent workloads as needed in PPVQ. Therefore we will explore the parallel performance of the MIC-aided implementation. With parallelization of the two most time-consuming modules of linear prediction and vector quantization in PPVQ, the total processing time of an AIRS granule can be compressed in less than 7.5 seconds which is equivalent to a speedup of ~8.8x. The use of MIC for PPVQ compression is thus promising as a low-cost and effective compression solution for ultraspectral sounder data for ground rebroadcast use.

An antidictionary is particularly useful for data compression, and on-line electrocardiogram (ECG) lossless compression algorithms using antidictionaries have been proposed. They work in real-time with constant memory and give better compression ratios than traditional lossless data compression algorithms, while they only deal with ECG data on a binary alphabet. This paper proposes on-line ECG lossless compression for a given data on a finite alphabet. The proposed algorithm gives not only better compression ratios than those algorithms but also uses less computational space than they do. Moreover, the proposed algorithm work in real-time. Its effectiveness is demonstrated by simulation results.

&lt;p&gt;The need of data compression at smart Edge/Fog-based gateways is undeniable as data compression can significantly reduce the amount of data that has to be transmitted over a network. This, in turn, has a direct impact on reducing transmission latency and increasing network bandwidth. In time-critical and data-sensitive IoT applications such as healthcare, lossless data compression is preferable as compressed data can be recovered without losing any information. However, it is not an easy task to choose a proper lossless data compression algorithm for IoT applications as each lossless data compression method has its own advantages and disadvantages. This paper focuses on the analysis of lossless data compression algorithms run at smart Edge/Fog gateways. Widely used lossless data compression is run at different hardware which is often used as smart Fog/Edge gateways. The latency of data compression and the compression rate in different cases of input data sizes are analyzed. The paper provides guidelines for choosing a proper lossless data compression algorithm for time-critical IoT applications. &lt;br /&gt;&lt;/p&gt;

INTRODUCTION: Genetic data plays a key role in the healthcare area in specific, but they are typically very large in size. Many research shows that absence of DNA information at the right time is one of the major causes of error in the healthcare area. The more genomics information that analysts secure, the better the prospects for individual and general wellbeing. Persevering and retrieving genetic information in the right form within the given time is a big challenge in the field of Healthcare. Effectively, pre-birth DNA tests screen for formative variations from the norm. Before long, patients will have their blood sequenced to detect any nonhuman DNA that may flag an irresistible illness. Later on, somebody managing malignancy will most likely track the movement of the sickness by having the DNA and RNA of single cells from various tissues sequenced every day. DNA sequencing of whole population will give a complete and better prediction of population wellbeing.OBJECTIVES: Hereditary data is growing exponentially; hence it is hard to deal with the consistently developing hereditary database. The human genome in its base configuration occupies almost thirty terabyte of storage space. Computational assets are constrained. Not just storage, transmission abilities and run time memory is likewise constrained. Data Compression is a test when the hereditary information is exponentially expanding. It is critical to save the integrity of hereditary information while packing it. Hence the main objective of this paper is to develop a lossless DNA compression algorithm that not only gives better compression but also help in retrieval of Information for efficient use in the area of Healthcare.METHODS: In this paper a lossless hereditary data compression method is being proposed. The proposed calculation works in a horizontal mode and utilization a reference based substitution technique for compression. The principle thought of this paper is in the kind of similarity scanned. All the predominant hereditary Compression methods search for similarity within the chromosome. These calculations either pursue flat mode or vertical mode for accomplishing compression. But whichever method the existing genetic compression algorithms use, they are all based on searching similarities within the chromosome i.e. they exploit only inter chromosomal similarities. The current studies focus will show that compression ratio achieved by analyzing individual chromosome is always less than the method in which we analyze and compress intra chromosomal similarities.RESULTS: This study shows that by simply using exactly matching repeats amongst all the chromosomes of the same genome, not only the compression ratio is improving but also a detailed study of all the similarities and differences between two genomes of the same species can be conducted.CONCLUSION: In this study, a new compression algorithm is being proposed for compressing DNA. Along with Inter chromosomal similarities, Intra chromosomal similarities are considered for this method. The results clearly shows that intra chromosomal matches are bigger and more than inter chromosomal matches which helps us to achieve better compression ratio.

A color-mapped image is composed of a palette and an image of indices. The color of each pixel is the palette color referenced by the pixel's index. The indices image is usually far from being smooth. This affects its compressibility by lossless image compression methods, due to their reliance on the natural smoothness of images. The indices images can be smoothed by re-ordering the palette, ie, assigning the indices to the colors in a better way. We propose a new method for palette ordering based on an approximate solution of the traveling salesman problem. The proposed method has low complexity, dependent only on the size of the palette. It is found to significantly improve the compression ratios of color-mapped images coded with the JPEG-LS lossless compression standard. The resulting compression ratios are better than those achieved by the popular GIF image file format.

In this paper, a method for lossless and near lossless compression of large digital mammograms is proposed. This method is based on a predictive coder that uses integer-to-integer operations, and generates a two-layer embedded bit stream (i.e. near-lossless and refinement layer). The maximum tolerated pixel distortion is guaranteed in the near lossless component. Comparisons with other proposed approaches are based on a database of high-resolution 12 bits/pixel digital mammograms. The simulation results indicate that our lossless compression method can offer average bit rates 39, 5%, 24%, 7% and 2.5% better than PNG, JPEG 2000, JPEG-lossless, and LOCO, respectively. Besides, for the same images, the proposed method offers near lossless compression at relatively low computational cost, providing an average bit rate of 2.57 bits/pixel and PSNR of 47.77 dB.

The article considers the relevance of the development of lossless image compression and transmission algorithms and their application for creating transport video surveillance systems. A brief overview of lossless transport image compression methods is provided. We propose a method for compressing transport plots based on the pyramid-recursive method of splitting the source image into polygons of various shapes and sizes. We consider two new algorithms for implementing the proposed method that are fundamentally different from each other: with a transition to the spectral region and without a transition to the spectral region of the original signal to ensure lossless compression. The results of testing various well-known lossless compression algorithms are analyzed: series length, Huffman, and arithmetic encoding, and compared with the proposed algorithms. It is shown that the proposed algorithms are more efficient in terms of compression ratio (2—3 times) compared to the known ones, while the computational complexity increases approximately by more than 3-4 times.