One efficient compression method used on WebGIS

Image compression schemes facilitate the transmission and storage of data in an efficient form by reducing redundant image data in the case of limited transmission bandwidth or storage space. However, due to the irreversible nature of lossy compression, these schemes also introduce various types of color artifacts into the reconstructed image at a high compression ratio. Consequently, a change in color information affects the gamut characteristic of the reconstructed image. Accordingly, this article investigates the relationship between the compression ratio and the gamut variation for a reconstructed image using JPEG and JPEG2000. To analyze the relationship between the compression ratio and the gamut variation, 18 color samples from the Macbeth ColorChecker are initially used as representative colors for all colors due to their uniform distribution in CIELAB color space. Based on the color information shift for representative color samples, 12 natural color images, classified into two groups depending on four color attributes, are used to investigate the relationship between the level of compression and the variation in the gamut area for reconstructed images. After determining the gamut areas for the decompressed images in relation to the compression ratio, gamut fidelity is obtained using the ratio of unique colors relative to the gamut area. Finally, the optimal least square method is applied to obtain fitted curves as an equation minimizing the error between the real data, the gamut area for the decompressed images, and the corresponding approximated values.

The performance of diesel engine highly depends on atomization, vaporization and mixing of fuel with air. These factors are strongly influenced by various parameters e.g. injection pressure, injection timing, compression ratio, equivalence ratio, cylinder geometry, in cylinder air motion etc. In this study, a diesel engine has been investigated by employing a commercial CFD software (ANSYS Forte, version 18.1) especially developed for internal combustion engines (ICE) modeling; focusing primarily on the effects of equivalence ratio and compression ratio on combustion and emission characteristics. RNG k-ε model was employed as the turbulence model for analyzing the physical phenomena involved in the change of kinetic energy. In order to reduce the computational cost and time, a sector mesh of 45o angle with periodic boundary conditions applied at the periodic faces of the sector, is considered instead of using the whole engine geometry. Simulations are performed for a range of equivalence ratio varying from 0.6 to 1.2 and for three compression ratios namely, 15:1, 18:1 and 21:1. Results show that, improvement in combustion characteristics with higher compression ratio could be achieved for both lean and rich mixtures. Peak in-cylinder pressure and peak heat release nearer to TDC are achieved for compression ratio of 18:1 that could results in more engine torque. For compression ratio beyond 16:1, effects of fuel concentration on ignition delay is more pronounced. At lower compression ratio, in-cylinder temperature is not sufficiently high for atomization, vaporization, mixing of fuel with air, and preflame reactions to occur immediately after the fuel injection. NOx emission in diesel engine increases due to higher pressure and temperature inside the cylinder associated with relatively higher compression ratio. Rich mixture leads to more CO and unburnt hydrocarbon emission compared to lean mixture as result of incomplete combustion. Engine operation with too high compression ratio is detrimental as emission is a major concern.

The problem of increasing the efficiency of coding of halftone images in the space of bit planes of differences in pixel values obtained using differential coding (DPCM – Differential pulse-code modulation) is considered. For a compact representation of DPCM pixel values, it is proposed to use a combined compression encoder that implements arithmetic coding and run-length coding. An arithmetic encoder provides high compression ratios, but has high computational complexity and significant encoding overhead. This makes it effective primarily for compressing the mean-value bit-planes of DPCM pixel values. Run-length coding is extremely simple and outperforms arithmetic coding in compressing long sequences of repetitive symbols that often occur in the upper bit planes of DPCM pixel values. For DPCM bit planes of pixel values of any image, a combination of simple run length coders and complex arithmetic coders can be selected that provides the maximum compression ratio for each bit plane and all planes in general with the least computational complexity. As a result, each image has its own effective combined encoder structure, which depends on the distribution of bits in the bit planes of the DPCM pixel values. To adapt the structure of the combined encoder to the distribution of bits in the bit planes of DPCM pixel values, the article proposes to use prediction of the volume of arithmetic code based on entropy and comparison of the obtained predicted value with the volume of run length code. The entropy is calculated based on the values of the number of repetitions of ones and zero symbols, which are obtained as intermediate results of the run length encoding. This does not require additional computational costs. It was found that in comparison with the adaptation of the combined encoder structure using direct determination of the arithmetic code volume of each bit plane of DPCM pixel values, the proposed encoder structure provides a significant reduction in computational complexity while maintaining high image compression ratios.

Image compression is the technique of reducing the number of bits required to represent a digital image, which can be accomplished by reducing the redundant and visually irrelevant information present in the images. In this study, a Wavelet Difference Reduction (WDR) coding based image compression technique is proposed which uses Principal Component Analysis (PCA). These techniques are combined in order to achieve high compression ratio with an acceptable degradation in the compressed images. The input image is first compressed using PCA. Few of the Principal Components are used to reconstruct the image. The reconstructed image obtained by PCA is further used as an input data for WDR compression. Since WDR is a wavelet based coding technique so it exploits local characteristics of the image and leads to high Compression Ratio (CR). Here, PCA and WDR are combined because PCA gives high image quality but low CR on the other hand WDR works opposite of it as it gives high CR. The proposed technique is applied on several test images and results are compared with WDR and JPEG2000 techniques. Results clearly show that the proposed technique is better than the existing techniques in terms of both objective and subjective fidelity criteria.

<p>The projection into 2030 of the climate data volume increase brings an important challenge to the climate science community. This is particularly true for the CMIP7 that is projected to need about an Exabyte of storage capacity. Error-bounded lossy compression is explored as a potential solution to the above problem by different climate research teams. Several lossy compression schemes have been proposed leveraging different forms of decorrelation (transforms, prediction, HoSVD, DNN), quantization (linear, non-linear, vector), and encoding (dictionary-based, variable length, etc.) algorithms. Our experience with different applications shows that the compression methods often need to be customized and optimized to fit the specificities of the datasets to compress and the user requirements on the compression quality, ratio, and throughput. However, none of the existing lossy compression software for scientific data has been designed to be customizable. To address this issue, we developed SZ3, an innovative customizable, modular compression framework. SZ3 is a full C++ refactoring of SZ2 enabling the specialization, addition, or removal of each stage of the lossy compression pipeline to fit the specific characteristics of the datasets to compress and the use-case requirements. This extreme flexibility allows adapting SZ3 to many different use-cases, from ultra-high compression for visualization to ultra-high-speed compression between the CPU (or GPU) and the memory. Thanks to its unique set of features: customization, high compression ratio, high compression throughput, and excellent accuracy preservation, SZ3 won a 2021 R&D100 award. In this presentation, we present SZ3 and a new data prediction-based decorrelation method that significantly improves the compression ratios for climate datasets over the state-of-the-art lossy compressors, while preserving the same data accuracy. Experiments based on CESM datasets show that SZ3 can lead to up to 300% higher compression ratios than SZ2 with the same compression error bound and similar compression throughput.</p>

The explosion of data, both in motion and in rest, along with the popularity of cloud computing, have resulted in the need for better in-line compression solutions. Current Huffman coding modes are optimal for a single metric: compression ratio (quality) or rate (performance). The ratio-focused mode, for example, further compresses the data by 15% as compared to the rate-focused mode, but takes 20% longer to execute.In this paper, we show how to balance the tradeoff between compression ratio and rate, without modifying existing standards and legacy decompression implementations. We present two Huffman encoding heuristics that achieve close-to-optimal compression rate and ratio (within 2%). Our proposed heuristics are practical - the first is better suited for hardware while the second for software implementations. We believe that such in-line compression heuristics can help enable the continued advancement of cloud computing and storage.

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.

This study proposes a novel deep-learning-based method for generating reduced representations of turbulent flows that ensures efficient storage and transfer while maintaining high accuracy during decompression. A Swin-transformer (ST) network combined with a physical constraints-based loss function is utilized to compress the turbulent flows with high compression ratios and then restore the data with underlying physical properties. The forced isotropic turbulence is used to demonstrate the ability of the ST-based model, where the instantaneous and statistical results show the excellent ability of the model to recover the flow data with a remarkable accuracy. Furthermore, the capability of the ST model is compared with a typical convolutional neural network-based auto-encoder (CNN-AE) by using the turbulent channel flow at two friction Reynolds numbers Reτ = 180 and 550. The results generated by the ST model are significantly more consistent with the direct numerical simulation data than those recovered by the CNN-AE, indicating the superior ability of the ST model to compress and restore the turbulent flow. This study also compares the compression performance of the ST model at different compression ratios (CR s) and finds that the model has low enough error even at very high CR. Additionally, the effect of transfer learning (TL) is investigated, showing that TL reduces the training time by 64% while maintaining high accuracy. The results illustrate for the first time that the Swin-transformer-based model incorporating a physically constrained loss function can compress and restore turbulent flows with the correct physics.

This paper proposes an image processing approach for compression of ECG signals based on 2D compression standards. This will explore both inter-beat and intra-beat redundancies that exist in the ECG signal leading to higher compression ratio (CR) as compared to 1D signal compression standards which explore only the inter-beat redundancies. The proposed method is twofold: In the first step, ECG signal is preprocessed and QRS detection is used to detect the peaks. In the second step, baseline wander is removed and a 2D array of data is obtained through the cut-and-align beat approach. Further beat reordering is done to arrange the ECG array depending upon the similarities available in the adjacent beats. Then ECG signal is compressed by first applying the lossless compression scheme called the 2D Run Length Encoding (RLE), and then a variant of discrete wavelet transform (DWT) called set partitioning in hierarchical trees (SPIHT) is applied to further compress the ECG signal. The proposed method is evaluated on the selected data from MITs Beth Israel Hospital, and it was conceded that this method surpasses some of the prevailing methods in the literature by attaining a higher compression ratio (CR) and moderate percentage-root-mean-square difference (PRD).

Data deduplication is a lossless compression technology that replaces the redundant data chunks with pointers pointing to the already-stored ones. Due to this intrinsic data elimination feature, the deduplication commodity would delinearize the data placement and force the data chunks that belong to the same data object to be divided into multiple separate parts. In our preliminary study, it is found that the de-linearization of the data placement would weaken the data spatial locality that is used for improving data read performance, deduplication throughput and efficiency in some deduplication approaches, which significantly affects the deduplication performance. In this paper, we first analyze the negative effect of the de-linearization of data placement to the data deduplication performance with some examples and experimental evidences, and then propose an effective approach to reduce the de-linearization of data placement by sacrificing little compression ratios. The experimental evaluation driven by the real world datasets shows that our proposed approach effectively reduces the de-linearization of the data placement and enhances the data spatial locality, which significantly improves the deduplication performances including deduplication throughput, deduplication efficiency and data read performance, while at the cost of little compression ratios.

: Time Difference of Arrival (TDOA) estimation is a pivotal technique with extensive applications across various domains, including passive detection and indoor positioning. For signals characterized by unknown modulation types and originating from non-cooperative sources, achieving high-precision TDOA estimation traditionally necessitates a substantial volume of sampling data. Conventional approaches, such as cross-correlation, require high sampling rates and extended durations, posing significant challenges in terms of data acquisition, transmission, and storage. To surmount these obstacles, this paper delves into an enhanced inexact reconstruction-based compressed sensing method for TDOA estimation (EIRCS), presents an optimized algorithmic procedure that further reduces the computational complexity of the EIRCS method. Experimental results substantiate that the EIRCS method is an unbiased estimation technique. It accomplishes high-precision TDOA estimation concurrent with efficient data compression. These insights suggest that the EIRCS method can yield dependable TDOA estimates with minimal error, even at elevated compression ratios. Its capacity to sustain high accuracy across a range of compression ratios renders it particularly apt for applications demanding efficient data processing and reliable TDOA estimation.

The current work investigated the characteristics of a diesel engine working with the ternary biodiesel blend having equal amounts of Jatropha, Karanja, and Cottonseed oils. The biodiesel constitutes 6.67% of each constituent oils and 80% diesel by volume. The impact of compression ratio on various characteristic parameters of a single cylinder diesel engine have been experimentally investigated to achieve enhanced performance and emission parameters. The compression ratio was varied from 15 to 18 and the results for the corresponding parameters were compared with the same parameters of the engine running with pure diesel at nominal compression ratio of 16. The brake thermal efficiency increased by 2.3% while the fuel consumption for unit power output reduced by 0.06 kg/kWh for the biodiesel at compression ratio 18. The hydrocarbon emission at compression ratio 18 with ternary blend biodiesel reduced by 37% than diesel while the carbon monoxide decreased by 42%. The oxides of nitrogen increased for biodiesel compared to pure diesel and it further increased with compression ratio due to better combustion of fuel. Thus, the biodiesel containing of 20% methyl esters of jatropha, karanja and cottonseed oil can be suitably adopted as an alternate fuel for automotive engines at higher compression ratio.

<div class="section abstract"><div class="htmlview paragraph">Alternative fuels such as LPG, CNG, hydrogen etc. have emerged as solutions to address depleting crude oil resources as well as deteriorating urban air quality. As a gaseous fuel, LPG has already been proven in terms of low emissions of carbon monoxide and hydrocarbon. The Government of India has taken an important and encouraging step towards the reduction of atmospheric pollution by directing its attention towards use of LPG as an automobile fuel. On Indian roads it has been found that, the vehicles that run on LPG have low compression ratio. It was also found that LPG could be directly retrofitted in existing gasoline fuelled engine and hence operating parameters such as compression ratio and ignition timing might affect performance and exhaust emission. So, the present work performed experiments to study the performance and emission characteristic of LPG fuelled SI engine at different compression ratios such as 9,10,11,12 and at ignition timing 10°BTDC, 20°BTDC, 30°BTDC and 40°BTDC. A single cylinder, 4-stroke, water-cooled, diesel engine converted to operate in SI mode was used; an arrangement was made to vary compression ratio and ignition timing. The results obtained shows that the engine running on an LPG fuel system has low brake thermal efficiency (4% - 10%) at lower compression ratio as compare to gasoline fuelled engine. But at higher compression ratio it was found to be increased in the range 13.18% to 40.78%. The CO (40% - 70%) and HC (70% - 80%) were found to be much lower as compared to gasoline fuelled engine.</div></div>

The transmission of image data acquired by remote sensing missions, based on space borne platforms is a major bottleneck, as a result of the limitation of on-board power, the huge volume of transmitted data, and the number of accessible ground receiving stations. In particular these arguments hold true for small satellites that faces additional design constraints in terms of size, mass and cost .To overcome downlink restriction, image compression has to be applied, although the provided data transmission rates are constantly growing, they can't keep up with the exponentially increasing data flood provided by the scanners, the reasons are twofold: First the acquisition rate exceeds the transmission rate. Second the satellite is not in constant visibility of ground receiving station, which effectively limits the downlinkable data volume per orbit. This paper introduces implementation of some of the compression techniques for both lossy and lossless which are used for the On-Board satellite missions, taking into consideration that the introduced compression method should support and enable the real time imaging and transmission. Also, a comparison study between both techniques is introduced based on measures like Compression Ratio (CR), Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR).The experimental results showed that: Huffman code is the most suitable code for lossless compression technique for satellite images, since it gives a considerable high compression ratio with respect to the other lossless algorithms (Run-Length Encode, Lempel-Ziv-Welch code and Arithmetic code). Huffman code does not require much storage capacity such as LZW or more computational requirements such as arithmetic code. Also, the most suitable lossy compression techniques for satellite images is applying Discrete Wavelet Transform (DWT) on the four Most Significant Bit Planes (MSPB) of the image then applying the arithmetic code over the resultant DWT coefficients. This technique achieves higher average compression ratio (8.28:1) than the two other techniques and at the same time the image quality is accepted.

Experimental investigation of the hydrogen-rich offgas spark ignition engine under the various compression ratios