Decompression Algorithm Research Articles

Алгоритм компрессии и декомпрессии потока конфигурационных данных ПЛИС

Алгоритм компрессии и декомпрессии потока конфигурационных данных ПЛИС

De-encapsulation and Decompression Algorithms for Level 0 Files of the ENVISAT Satellite in Image Mode

This document describes in detail the structure of the level 0 data of the ENVISAT satellite and, in addition, presents the de-encapsulation and decompression algorithms of the raw ASAR data, specifically the IM operation mode files. These algorithms were developed in order to facilitate and optimize the task of retrieving the source data. As a result, a raw-data-based image of a region in the Falcon state of Venezuela was obtained after applying the aforementioned algorithms to the level 0 files downloaded by the ESA Simple Online Catalogue's.

Static Metabolic Bubbles as Precursors of Vascular Gas Emboli During Divers' Decompression: A Hypothesis Explaining Bubbling Variability.

IntroductionThe risk for decompression sickness (DCS) after hyperbaric exposures (such as SCUBA diving) has been linked to the presence and quantity of vascular gas emboli (VGE) after surfacing from the dive. These VGE can be semi-quantified by ultrasound Doppler and quantified via precordial echocardiography. However, for an identical dive, VGE monitoring of divers shows variations related to individual susceptibility, and, for a same diver, dive-to-dive variations which may be influenced by pre-dive pre-conditioning. These variations are not explained by currently used algorithms. In this paper, we present a new hypothesis: individual metabolic processes, through the oxygen window (OW) or Inherent Unsaturation of tissues, modulate the presence and volume of static metabolic bubbles (SMB) that in turn act as precursors of circulating VGE after a dive.MethodsWe derive a coherent system of assumptions to describe static gas bubbles, located on the vessel endothelium at hydrophobic sites, that would be activated during decompression and become the source of VGE. We first refer to the OW and show that it creates a local tissue unsaturation that can generate and stabilize static gas phases in the diver at the surface. We then use Non-extensive thermodynamics to derive an equilibrium equation that avoids any geometrical description. The final equation links the SMB volume directly to the metabolism.Results and DiscussionOur model introduces a stable population of small gas pockets of an intermediate size between the nanobubbles nucleating on the active sites and the VGE detected in the venous blood. The resulting equation, when checked against our own previously published data and the relevant scientific literature, supports both individual variation and the induced differences observed in pre-conditioning experiments. It also explains the variability in VGE counts based on age, fitness, type and frequency of physical activities. Finally, it fits into the general scheme of the arterial bubble assumption for the description of the DCS risk.ConclusionMetabolism characterization of the pre-dive SMB population opens new possibilities for decompression algorithms by considering the diver’s individual susceptibility and recent history (life style, exercise) to predict the level of VGE during and after decompression.

OpenCL을 사용한 Intel FPGA SoC 기반 실시간 병렬 다상분해 주파수 대역압축 및 복원 알고리즘

OpenCL을 사용한 Intel FPGA SoC 기반 실시간 병렬 다상분해 주파수 대역압축 및 복원 알고리즘

Three Dimensional Point Cloud Compression and Decompression Using Polynomials of Degree One

The availability of cheap depth range sensors has increased the use of an enormous amount of 3D information in hand-held and head-mounted devices. This has directed a large research community to optimize point cloud storage requirements by preserving the original structure of data with an acceptable attenuation rate. Point cloud compression algorithms were developed to occupy less storage space by focusing on features such as color, texture, and geometric information. In this work, we propose a novel lossy point cloud compression and decompression algorithm that optimizes storage space requirements by preserving geometric information of the scene. Segmentation is performed by using a region growing segmentation algorithm. The points under the boundary of the surfaces are discarded that can be recovered through the polynomial equations of degree one in the decompression phase. We have compared the proposed technique with existing techniques using publicly available datasets for indoor architectural scenes. The results show that the proposed novel technique outperformed all the techniques for compression rate and RMSE within an acceptable time scale.

Validation of algorithms used in commercial off-the-shelf dive computers.

Whilst the US Navy has been very systematic about validating Navy dive computer algorithms, there has been little documented or published evidence of rigorous testing of the algorithms in commercial off-the-shelf dive computers. This paper reports the evaluation of four algorithms used in these - Bühlmann ZHL-16C; VPM-B; Suunto-RGBM; EMC-20H - by comparison with US Navy experimental dives with known decompression sickness outcomes. Three specific tests were developed to test the algorithms' ability to mitigate decompression sickness: Total decompression time; no stop times and first stop depth. Output of commercial decompression algorithms were compared to either the probability of decompression sickness (PDCS) results from US Navy man-trials or statistical models derived from PDCS data. The algorithms were first tested with default conservative factors, then these factors were adjusted if the tests were not initially passed. The last verification step was to compare the output of the wrist computer with that of the full desktop algorithm. This testing indicated that, whilst none of the four passed all of the proposed tests with factory-default conservatism, ZHL-16C and Suunto-RGBM could be made to pass by adjusting user-defined settings. Man-trial data on PDCS is available to the non-US Navy scientific community for testing of commercial decompression algorithms. This type of validation testing can be very informative on how to best use available commercial dive computers to improve diver safety.

Time Optimization for Lossy Decompression of the LISA Sensor Data on LAPAN A3 Satellite Using a Grouping Method of HUFFMAN Code Bit Number

The LAPAN-A3 satellite provides compressed multispectral data from LISA sensor using real-time lossy compression. The compression of the multispectral data of radiometric resolution 12bit/pixel is built from the Fourier transform and the use of Huffman decoder 514 binary length code. A problem arised in the data extraction process, that decompression performance is very slow because the search method of code value in Hufman table was done sequentially from one bit to the next bit in one block of data along 4000 pixels. The data extraction time for one scene in 12 minutes acquisition duration (one full path) takes up to 20 hours. This paper proposes a method of improving the LISA real-time lossy data decompression algorithm using the grouping method of bit code on the Huffman decoding algorithm and using pointer for reading data in the buffer memory. Using this method, the searching process of bit code for all characters in the Huffman decoder algorithm is done regularly, so the search processing time is significantly reduced. The performance test used 6 data samples. The result showed that extraction time has an average of 14 times faster. The lossy compression ratio is still in accordance with the design specification of LISA sensor that is less than 4 times and the appearance of the special character is very small i.e. less than 0.5%.

Dive computer decompression models and algorithms: philosophical and practical views

Dive computer decompression models and algorithms: philosophical and practical views

Performance optimization of dual stage algorithm for lossless data compression and decompression

In this paper, an optimized dual stage architecture is proposed which is the combination of Lempel-Ziv-Welch (LZW) Algorithm at the first phase and Arithmetic Coding being the later part of Architecture. LZW Algorithm is a lossless compression algorithm and code here for each character is available in the dictionary which reduces 5-bits per cycle as compared to ASCII. In arithmetic coding the numbers are represented by an interval of real numbers from zero to one according to their probabilities. It is an entropy coding and is lossless in nature. The text information is allowed to pass through the proposed architecture and it gets compressed to the higher rate.

Compressed Animated Light Fields with Real-Time View-Dependent Reconstruction.

We propose an end-to-end solution for presenting movie quality animated graphics to the user while still allowing the sense of presence afforded by free viewpoint head motion. By transforming offline rendered movie content into a novel immersive representation, we display the content in real-time according to the tracked head pose. For each frame, we generate a set of cubemap images per frame (colors and depths) using a sparse set of of cameras placed in the vicinity of the potential viewer locations. The cameras are placed with an optimization process so that the rendered data maximise coverage with minimum redundancy, depending on the lighting environment complexity. We compress the colors and depths separately, introducing an integrated spatial and temporal scheme tailored to high performance on GPUs for Virtual Reality applications. A view-dependent decompression algorithm decodes only the parts of the compressed video streams that are visible to users. We detail a real-time rendering algorithm using multi-view ray casting, with a variant that can handle strong view dependent effects such as mirror surfaces and glass. Compression rates of 150:1 and greater are demonstrated with quantitative analysis of image reconstruction quality and performance.

Compression and Conditional Emulation of Climate Model Output

ABSTRACTNumerical climate model simulations run at high spatial and temporal resolutions generate massive quantities of data. As our computing capabilities continue to increase, storing all of the data is not sustainable, and thus it is important to develop methods for representing the full datasets by smaller compressed versions. We propose a statistical compression and decompression algorithm based on storing a set of summary statistics as well as a statistical model describing the conditional distribution of the full dataset given the summary statistics. We decompress the data by computing conditional expectations and conditional simulations from the model given the summary statistics. Conditional expectations represent our best estimate of the original data but are subject to oversmoothing in space and time. Conditional simulations introduce realistic small-scale noise so that the decompressed fields are neither too smooth nor too rough compared with the original data. Considerable attention is paid to accurately modeling the original dataset—1 year of daily mean temperature data—particularly with regard to the inherent spatial nonstationarity in global fields, and to determining the statistics to be stored, so that the variation in the original data can be closely captured, while allowing for fast decompression and conditional emulation on modest computers. Supplementary materials for this article are available online.

Efficient image compression and decompression algorithms for OCR systems

This paper presents an efficient new image compression and decompression methods for document images, intended for usage in the pre-processing stage of an OCR system designed for needs of the ?Nikola Tesla Museum? in Belgrade. Proposed image compression methods exploit the Run-Length Encoding (RLE) algorithm and an algorithm based on document character contour extraction, while an iterative scanline fill algorithm is used for image decompression. Image compression and decompression methods are compared with JBIG2 and JPEG2000 image compression standards. Segmentation accuracy results for ground-truth documents are obtained in order to evaluate the proposed methods. Results show that the proposed methods outperform JBIG2 compression regarding the time complexity, providing up to 25 times lower processing time at the expense of worse compression ratio results, as well as JPEG2000 image compression standard, providing up to 4-fold improvement in compression ratio. Finally, time complexity results show that the presented methods are sufficiently fast for a real time character segmentation system.

2D-RBUC for efficient parallel compression of residuals

In this paper, we present a method for lossless compression of residuals with an efficient SIMD parallel decompression. The residuals originate from lossy or near lossless compression of height fields, which are commonly used to represent models of terrains. The algorithm is founded on the existing RBUC method for compression of non-uniform data sources. We have adapted the method to capture 2D spatial locality of height fields, and developed the data decompression algorithm for modern GPU architectures already present even in home computers. In combination with the point-level SIMD-parallel lossless/lossy high field compression method HFPaC, characterized by fast progressive decompression and seamlessly reconstructed surface, the newly proposed method trades off small efficiency degradation for a non negligible compression ratio (measured up to 91%) benefit.

Dive Risk Factors, Gas Bubble Formation, and Decompression Illness in Recreational SCUBA Diving: Analysis of DAN Europe DSL Data Base.

Introduction: The popularity of SCUBA diving is steadily increasing together with the number of dives and correlated diseases per year. The rules that govern correct decompression procedures are considered well known even if the majority of Decompression Sickness (DCS) cases are considered unexpected confirming a bias in the “mathematical ability” to predict DCS by the current algorithms. Furthermore, little is still known about diving risk factors and any individual predisposition to DCS. This study provides an in-depth epidemiological analysis of the diving community, to include additional risk factors correlated with the development of circulating bubbles and DCS.Materials and Methods: An originally developed database (DAN DB) including specific questionnaires for data collection allowed the statistical analysis of 39,099 electronically recorded open circuit dives made by 2,629 European divers (2,189 males 83.3%, 440 females 16.7%) over 5 years. The same dive parameters and risk factors were investigated also in 970 out of the 39,099 collected dives investigated for bubble formation, by 1-min precordial Doppler, and in 320 sea-level dives followed by DCS symptoms.Results: Mean depth and GF high of all the recorded dives were 27.1 m, and 0.66, respectively; the average ascent speed was lower than the currently recommended “safe” one (9–10 m/min). We found statistically significant relationships between higher bubble grades and BMI, fat mass, age, and diving exposure. Regarding incidence of DCS, we identified additional non-bubble related risk factors, which appear significantly related to a higher DCS incidence, namely: gender, strong current, heavy exercise, and workload during diving. We found that the majority of the recorded DCS cases were not predicted by the adopted decompression algorithm and would have therefore been defined as “undeserved.”Conclusion: The DAN DB analysis shows that most dives were made in a “safe zone,” even if data show an evident “gray area” in the “mathematical” ability to predict DCS by the current algorithms. Some other risk factors seem to influence the possibility to develop DCS, irrespective of their effect on bubble formation, thus suggesting the existence of some factors influencing or enhancing the effects of bubbles.

Adaptive loss‐less data compression method optimized for GPU decompression

SummaryThere is no doubt that data compression is very important in computer engineering. However, most lossless data compression and decompression algorithms are very hard to parallelize, because they use dictionaries updated sequentially. The main contribution of this paper is to present a new lossless data compression method that we call adaptive loss‐less (ALL) data compression. It is designed so that the data compression ratio is moderate, but decompression can be performed very efficiently on the graphics processing unit (GPU). This makes sense for applications such as training of deep learning, in which compressed archived data are decompressed many times. To show the potentiality of ALL data compression method, we have evaluated the running time using five images and five text data and compared ALL with previously published lossless data compression methods implemented in the GPU, Gompresso, CULZSS, and LZW. The data compression ratio of ALL data compression is better than the others for eight data out of these 10 data. Also, our GPU implementation on GeForce GTX 1080 GPU for ALL decompression runs 84.0 to 231 times faster than the CPU implementation on Core i7‐4790 CPU. Further, it runs 1.22 to 23.5 times faster than Gompresso, CULZSS, and LZW running on the same GPU.

Cluster-Based Quality-Aware Adaptive Data Compression for Streaming Data

Wireless sensor networks (WSNs) are widely applied in data collection applications. Energy efficiency is one of the most important design goals of WSNs. In this article, we examine the tradeoffs between the energy efficiency and the data quality. First, four attributes used to evaluate data quality are formally defined. Then, we propose a novel data compression algorithm, Quality-Aware Adaptive data Compression (QAAC), to reduce the amount of data communication to save energy. QAAC utilizes an adaptive clustering algorithm to build clusters from dataset; then a code for each cluster is generated and stored in a Huffman encoding tree. The encoding algorithm encodes the original dataset based on the Haffman encoding tree. An improvement algorithm is also designed to reduce the information loss when data are compressed. After the encoded data, the Huffman encoding tree and parameters used in the improvement algorithm have been received at the sink, a decompression algorithm is used to retrieve the approximation of the original dataset. The performance evaluation shows that QAAC is efficient and achieves a much higher compression ratio than lossy and lossless compression algorithms, while it has much smaller information loss than lossy compression algorithms.

A speculative parallel decompression algorithm on Apache Spark

Data decompression is one of the most important techniques in data processing and has been widely used in multimedia information transmission and processing. However, the existing decompression algorithms on multicore platforms are time-consuming and do not support large data well. In order to expand parallelism and enhance decompression efficiency on large-scale datasets, based on the software thread-level speculation technique, this paper raises a speculative parallel decompression algorithm on Apache Spark. By analyzing the data structure of the compressed data, the algorithm firstly hires a function to divide compressed data into blocks which can be decompressed independently and then spawns a number of threads to speculatively decompress data blocks in parallel. At last, the speculative results are merged to form the final outcome. Comparing with the conventional parallel approach on multicore platform, the proposed algorithm is very efficiency and obtains a high parallelism degree by making the best of the resources of the cluster. Experiments show that the proposed approach could achieve 2.6\(\times \) speedup when comparing with the traditional approach in average. In addition, with the growing number of working nodes, the execution time cost decreases gradually, and the speedup scales linearly. The results indicate that the decompression efficiency can be significantly enhanced by adopting this speculative parallel algorithm.

Fast Bayesian JPEG Decompression and Denoising With Tight Frame Priors.

JPEG decompression can be understood as an image reconstruction problem similar to denoising or deconvolution. Such problems can be solved within the Bayesian maximum a posteriori probability framework by iterative optimization algorithms. Prior knowledge about an image is usually described by the l1 norm of its sparse domain representation. For many problems, if the sparse domain forms a tight frame, optimization by the alternating direction method of multipliers can be very efficient. However, for JPEG, such solution is not straightforward, e.g., due to quantization and subsampling of chrominance channels. Derivation of such solution is the main contribution of this paper. In addition, we show that a minor modification of the proposed algorithm solves simultaneously the problem of image denoising. In the experimental section, we analyze the behavior of the proposed decompression algorithm in a small number of iterations with an interesting conclusion that this mode outperforms full convergence. Example images demonstrate the visual quality of decompression and quantitative experiments compare the algorithm with other state-of-the-art methods.

Near-lossless compression/decompression algorithms for digital data transmitted over fronthaul in C-RAN

The cloud radio access network (C-RAN) is not only a very important deployment solution for the future RAN but is also a core platform for network-centric advanced transmission techniques such as coordinated multi-point transmission and reception and the distributed antenna system. One of the main issues when implementing C-RAN at low cost and high efficiency is the need to reduce the implementation cost of the fronthaul and improve its usage efficiency. In order to achieve this, in this paper, near-lossless compression and decompression algorithms for digital data transported via fronthaul in C-RAN are proposed, where the compression is mainly achieved through the removal of various redundancies in wireless communication signals. Since the proposed algorithms significantly reduce the amount of data that should be transmitted via fronthaul while maintaining negligible in-band distortion in terms of error vector magnitude (EVM), we can actually reduce the number of transmission lines or enhance the utilization of them. In addition, they can be operated with a minimum compression ratio as well as a constant compression ratio; therefore, real-time processing and fronthaul data-muxing can be easily performed. Simulation results and comparisons have been carried out based on the 3rd generation partnership project long-term evolution system and the common public radio interface, which is a publicly available specification that is widely utilized to implement the fronthaul. Simulation results confirm that the proposed schemes can provide remarkable compression performance with a zero uncoded bit error rate and negligible signal distortion. Finally, the proposed schemes have various parameters that can be adjusted to meet given requirements such as latency, the compression ratio, EVM, complexity, and so on, and thus a smooth tradeoff between them can be achieved.

An optimal representation for the trace zero subgroup

We give an optimal-size representation for the elements of the trace zero subgroup of the Picard group of an elliptic or hyperelliptic curve of any genus, with respect to a field extension of any prime degree. The representation is via the coefficients of a rational function, and it is compatible with scalar multiplication of points. We provide efficient compression and decompression algorithms, and complement them with implementation results. We discuss in detail the practically relevant cases of small genus and extension degree, and compare with the other known compression methods.