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Hybrid-Smash: A Heterogeneous CPU-GPU compression Library

IEEE ACCESS 2024年 12卷 32706-32723页

作者： Penaranda, Cristian Reano, Carlos Silla, Federico Univ Politecn Valencia DISCA Valencia 46022 Spain Univ Valencia Dept Informat ETSE Burjassot 46100 Spain

compression algorithms are widely used to reduce data size and improve application performance. Nevertheless, data compression has a computational cost which can limit its use. GPUs could be leveraged to reduce compression time. However, existing GPU-based compression libraries expect data to compress in GPU memory, although it is usually stored in CPU memory. Additionally, setup time of GPUs could be a problem when compressing small data sizes. In this paper, we implement a new GPU-based compression library. Contrary to existing ones, our library uses data located in CPU memory. Performance results show that, for the same compression algorithms, GPUs are beneficial for larger data sizes whereas smaller data sizes are compressed faster using CPUs. Therefore, we enhance our proposal with Hybrid-Smash: a heterogeneous CPU-GPU compression library, which transparently uses CPU or GPU compression depending on data size, thus improving compression for any data size.

关键词： Graphics processing units Image coding compression algorithms Symbols Proposals Artificial intelligence Parallel processing Performance evaluation Central Processing Unit Hybrid intelligent systems Data systems Size control Lossless compression parallel computing GPU

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Lossless image compression by using gradient adjusted prediction and Burrows-Wheeler transformation

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1999年第2期45卷 380-386页

作者： Ng, KS Cheng, LM City Univ Hong Kong Dept Elect Engn 83 Tat Chee Ave Kowloon Peoples R China

In generally text compression techniques cannot be used directly in image compression because the model of text and image are different. Recently, a new class of text compression, namely, block-sorting algorithm which involves Burrows and Wheeler transformation (BWT) gives excellent results in text compression. However, if we apply it directly into image compression, the result is poor. Surprisingly, good results can be obtained if we employ a prediction model such as the one defined in JPEG standard before the BWT algorithm. Thus, the predictive model plays critical role - in the compression process. To further improve the compression efficiency, we use Gradient Adjusted Prediction (GAP). Experimental results show that the proposed method is better than lossless JPEG and some LZ-based compression methods.

关键词： Image coding Transform coding Sorting Predictive models compression algorithms Data compression ISO standards IEC standards Entropy coding Performance loss

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Feature preserving image compression

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PATTERN RECOGNITION LETTERS 2003年第15期24卷 2767-2776页

作者： Namuduri, KR Ramaswamy, VN Wichita State Univ ECE Dept Wichita KS 67260 USA Comcast Corp Comcast IP Serv Broadband Engn Cherry Hill NJ 08002 USA

Image details appear as wavelet coefficients with large magnitude in the wavelet transform domain. Image compression methods such as the embedded zerotree wavelet encoding and the set partitioning in hierarchical trees select wavelet coefficients in the order of their significance (magnitude) and encode them generating an embedded bit stream. In existing wavelet based image compression techniques, the significance of a wavelet coefficient is solely defined by its magnitude. In this paper, we describe a flexible scheme to prioritize wavelet coefficients based on the features they exhibit. The proposed scheme combines tree based wavelet coefficient representation with the implicit transmission of data about image features that need to be emphasized. The experimental results presented in this paper demonstrate that it is possible to enhance the image features in the reconstructed images by embedding locally adaptive image processing techniques in the compression algorithm. The main advantage of the proposed technique over the existing methods is that it exploits the embedded zerotree data structure to eliminate the need to send side (additional) information to the decoder regarding the feature selection process. (C) 2003 Published by Elsevier B.V.

关键词： wavelets Wavelet coefficients bit stream embedding decoders Process selection set partitioning image compression method compression algorithms image feature

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Universal Graph compression: Stochastic Block Models

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第2期70卷 1473-1497页

作者： Bhatt, Alankrita Wang, Ziao Wang, Chi Wang, Lele Calif Inst Technol Caltech Pasadena CA 91125 USA Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Microsoft Res Redmond WA 98052 USA

Motivated by the prevalent data science applications of processing large-scale graph data such as social networks and biological networks, this paper investigates lossless compression of data in the form of a labeled graph. Particularly, we consider a widely used random graph model, stochastic block model (SBM), which captures the clustering effects in social networks. An information-theoretic universal compression framework is applied, in which one aims to design a single compressor that achieves the asymptotically optimal compression rate, for every SBM distribution, without knowing the parameters of the SBM. Such a graph compressor is proposed in this paper, which universally achieves the optimal compression rate with polynomial time complexity for a wide class of SBMs. Existing universal compression techniques are developed mostly for stationary ergodic one-dimensional sequences. However, the adjacency matrix of SBM has complex two-dimensional correlations. The challenge is alleviated through a carefully designed transform that converts two-dimensional correlated data into almost i.i.d. submatrices. The sequence of submatrices is then compressed by a Krichevsky-Trofimov compressor, whose length analysis is generalized to identically distributed but arbitrarily correlated sequences. In four benchmark graph datasets, the compressed files from competing algorithms take 2.4 to 27 times the space needed by the proposed scheme.

关键词： compression algorithms source coding graph theory

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An MCMC Approach to Universal Lossy compression of Analog Sources

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2012年第10期60卷 5230-5240页

作者： Baron, Dror Weissman, Tsachy N Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27607 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA

Motivated by the Markov Chain Monte Carlo (MCMC) approach to the compression of discrete sources developed by Jalali and Weissman, we propose a lossy compression algorithm for analog sources that relies on a finite reproduction alphabet, which grows with the input length. The algorithm achieves, in an appropriate asymptotic sense, the optimum Shannon theoretic tradeoff between rate and distortion, universally for stationary ergodic continuous amplitude sources. We further propose an MCMC-based algorithm that resorts to a reduced reproduction alphabet when such reduction does not prevent achieving the Shannon limit. The latter algorithm is advantageous due to its reduced complexity and improved rates of convergence when employed on sources with a finite and small optimum reproduction alphabet.

关键词： compression algorithms rate distortion simulated annealing

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AN ADAPTIVE REAL-TIME ECG compression ALGORITHM WITH VARIABLE THRESHOLD

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1988年第6期35卷 489-494页

作者： FURHT, B PEREZ, A SOFTWARE & INSTRUMENT DESIGN MIAMIFL 33152

A real-time compression algorithm has been developed which is suitable for both real-time ECG (electrocardiogram) transmission and ECG data storing. The algorithm represents a modification of the amplitude zone time epoch coding (AZTEC) technique extended with several statistical parameters used to calculate the variable threshold. The proposed algorithm has been applied in the design of a pacemaker followup system for the online ECG data transmission from the pacemaker implanted in a human being to the computer system located at the clinic.< >

关键词： Electrocardiography compression algorithms Pacemakers Algorithm design and analysis Data communication Humans

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ADAPTIVE compression CODING

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IEEE TRANSACTIONS ON COMMUNICATIONS 1991年第8期39卷 1245-1254页

作者： NASIOPOULOS, P WARD, RK MORSE, DJ UNIV BRITISH COLUMBIA DEPT ELECT ENGNVANCOUVER V6T 1W5BCCANADA

A compression technique which preserves edges in compressed pictures is developed. It is desirable to build edge preservation characteristics in compression methods since many applications in engineering and vision depend on edge information. In this paper we present a compression algorithm which adapts itself to the local nature of the image. Smooth regions are represented by their averages and edges are preserved using quad trees. Textured regions are encoded using BTC (block truncation coding) and a modification of BTC using look-up tables. We developed the latter (BTC with look-up tables) so as 1) to improve on the compression ratio of BTC and 2) to leave the visual quality of compression exactly the same as that of BTC. A threshold using a range which is the difference between the maximum and the minimum grey levels in a 4 x 4 pixel quadrant is used. At the recommended value of the threshold (equal to 18), the quality of the compressed textured regions is very high, the same as that of AMBTC (absolute moment block truncation coding) but the edge preservation quality is far superior to that of AMBTC. This significant improvement is achieved at compression levels (1.1-1.2 b/pixel) which are better than that of AMBTC (1.63). compression levels below (0.5-0.8) b/pixel may be achieved. The high quality of edge preservation of this method does not change at these low compression levels or as the threshold value changes. However, a postfilter is needed to improve the blocky appearance in the smooth and textured regions.

关键词： Image coding Image edge detection Quantization Biomedical engineering Military aircraft compression algorithms Helium Aerospace engineering Robot vision systems Aircraft propulsion

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Real-Time Lossless compression for Ultrahigh-Density Synchrophasor and Point-on-Wave Data

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第2期69卷 2012-2021页

作者： Chen, Chang Wang, Weikang Yin, He Zhan, Lingwei Liu, Yilu Univ Tennessee Knoxville TN 37996 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA

Modern advanced phasor measurement units are developed with ultrahigh reporting rates to meet the demand for monitoring the power systems dynamics in detail. Due to the large volume of data, the communication and storage systems are seriously challenged with the presence of ultrahigh-density (UHD) synchrophasor and point-on-wave (POW) data. Therefore, it is an urgent task to compress the UHD data for more efficient communication and data storage. This article proposes several methods to compress the synchrophasor and POW data in a lossless manner. First, an improved time-series special compression (ITSSC) method is proposed to compress the UHD frequency data. Second, a delta-difference Huffman method is combined with the time-series special compression algorithm to compress the UHD phase angle data. Finally, a cyclical high-order delta modulation method is proposed to compress the UHD POW data. The proposed models are extensively tested and compared with different existing lossless compression algorithms using the field-collected synchrophasor and POW data at different reporting rates. The results indicate that the proposed algorithms are efficient in performing lossless compression for the UHD synchrophasor and POW data in real time.

关键词： Phasor measurement units Real-time systems Encoding Prediction algorithms Entropy compression algorithms Tools Huffman encoding method improved time-series special compression (ITSSC) method lossless data compression point-on-wave (POW) data synchrophasor data ultrahigh density

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SCALABLE compression FOR IMAGE BROWSING

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1994年第3期40卷 394-404页

作者： JAIN, A PANCHANATHAN, S Department of Electrical Engineering University of Ottawa Ottawa ONT Canada

In this paper, we present scalable compression algorithms for image browsing. Recently, the International Standards Organization (ISO) has proposed the JPEG standard for still image compression. JPEG standard not only provides the basic feature of compression (baseline algorithm) but also provides the framework for reconstructing images in different picture qualities and sizes. These features are referred to as SNR and spatial scalability, respectively. SNR scalability and spatial scalability can be implemented using the progressive and hierarchical modes in the JPEG standard. In this paper, we implement and investigate the performance of the progressive and hierarchical coding modes of the JPEG standard and compare their performance with the baseline algorithm,

关键词： Image coding Transform coding Scalability Image reconstruction ISO standards Image storage Video compression Signal to noise ratio compression algorithms Multimedia databases

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compression of color skin tumor images with vector quantization - Performing compression in DWT and DCT domains with a goal of reducing storage space for and transmissions costs of digital images

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IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE 2001年第6期20卷 152-164页

作者： Guo, L Umbaugh, S Cheng, Y So Illinois Univ Dept Elect & Comp Engn Edwardsville IL 62026 USA

Performing compression in DWT and DCT domains is shown to reduce storage space and transmissions cost of digital images.

关键词： Image coding Skin neoplasms Vector quantization Image storage Discrete cosine transforms Ultrasonic imaging Testing Transform coding Costs compression algorithms

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