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An Efficient Adaptive Binary range coder and Its VLSI Architecture

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2015年第8期25卷 1435-1446页

作者： Belyaev, Evgeny Liu, Kai Gabbouj, Moncef Li, YunSong Tampere Univ Technol Dept Signal Proc Tampere 33720 Finland Xidian Univ Sch Comp Xian 710071 Peoples R China

In this paper, we propose a new hardware-efficient adaptive binary range coder (ABRC) and its very-large-scale integration (VLSI) architecture. To achieve this, we follow an approach that allows to reduce the bit capacity of the multiplication needed in the interval division part and shows how to avoid the need to use a loop in the renormalization part of ABRC. The probability estimation in the proposed ABRC is based on a lookup table free virtual sliding window. To obtain a higher compression performance, we propose a new adaptive window size selection algorithm. In comparison with an ABRC with a single window, the proposed system provides a faster probability adaptation at the initial encoding/decoding stage, and more accurate probability estimation for very low entropy binary sources. We show that the VLSI architecture of the proposed ABRC attains a throughput of 105.92 MSymbols/s on the FPGA platform, and consumes 18.15 mW for the dynamic part power. In comparison with the state-of-the-art MQ-coder (used in JPEG2000 standard) and the M-coder (used in H.264/Advanced Video Coding and H.265/High Efficiency Video Coding standards), the proposed ABRC architecture provides comparable throughput, reduced memory, and power consumption. Experimental results obtained for a wavelet video codec with JPEG2000-like bit-plane entropy coder show that the proposed ABRC allows to reduce the bit rate by 0.8%-8% in comparison with the MQ-coder and from 1.0%-24.2% in comparison with the M-coder.

关键词： Arithmetic coding image/video compression range coder very-large-scale integration (VLSI) architecture

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Fast and efficient compression of floating-point data

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2006年第5期12卷 1245-1250页

作者： Lindstrom, Peter Isenburg, Martin Lawrence Livermore Natl Lab Livermore CA 94551 USA Univ Calif Berkeley Berkeley CA 94720 USA

Large scale scientific simulation codes typically run on a cluster of CPUs that write/read time steps to/from a single file system. As data sets are constantly growing in size, this increasingly leads to I/O bottlenecks. When the rate at which data is produced exceeds the available I/O bandwidth, the simulation stalls and the CPUs are idle. Data compression can alleviate this problem by using some CPU cycles to reduce the amount of data needed to be transfered. Most compression schemes, however, are designed to operate offline and seek to maximize compression, not throughput. Furthermore, they often require quantizing floating-point values onto a uniform integer grid, which disqualifies their use in applications where exact values must be retained. We propose a simple scheme for lossless, online compression of floating-point data that transparently integrates into the I/O of many applications. A plug-in scheme for data-dependent prediction makes our scheme applicable to a wide variety of data used in visualization, such as unstructured meshes, point sets, images, and voxel grids. We achieve state-of-the-art compression rates and speeds, the latter in part due to an improved entropy coder. We demonstrate that this significantly accelerates I/O throughput in real simulation runs. Unlike previous schemes, our method also adapts well to variable-precision floating-point and integer data.

关键词： high throughput lossless compression file compaction for I/O efficiency fast entropy coding range coder predictive coding large scale simulation and visualization

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Fast and efficient compression of floating-point data

Fast and efficient compression of floating-point data

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IEEE Visualization Conference (Vis 2006)/IEEE Symposium on Information Visualization (InfoVis 2006)

作者： Lindstrom, Peter Isenburg, Martin Lawrence Livermore Natl Lab Livermore CA 94551 USA Univ Calif Berkeley Berkeley CA 94720 USA

关键词： high throughput lossless compression file compaction for I/O efficiency fast entropy coding range coder predictive coding large scale simulation and visualization

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Complexity Analysis of Adaptive Binary Arithmetic Coding Software Implementations

Complexity Analysis of Adaptive Binary Arithmetic Coding Sof...

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11th Next Generation Teletraffic and Wired/Wireless Advanced Networking (NEW2AN) / 4th Conference on Smart Spaces (ruSMART)

作者： Belyaev, Evgeny Veselov, Anton Turlikov, Andrey Kai, Liu Tampere Univ Technol Korkeakoulunkatu 10 Tampere 33720 Finland Univ Aerosp Instrumentat St Petersburg Russia Xidian Univ Sch Comp Sci & Technol Xian Peoples R China

ISBN: (纸本)9783642228759

This paper is dedicated to the complexity comparison of adaptive binary arithmetic coding integer software implementations. Firstly, for binary memoryless sources with known probability distribution, we prove that encoding time for arithmetic encoder is a linear function of a number of input binary symbols and source entropy. Secondly, we show that the byte-oriented renormalization allows to decrease encoding time up to 40% in comparison with bit-oriented renormalization. Finally, we study influence of probability estimation algorithm for encoding time and show that probability estimation algorithm using "Virtual Sliding Window" has less computation complexity than state machine based probability estimation algorithm from H.264/AVC standard.

关键词： binary arithmetic coder range coder probability estimation complexity analysis

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基于3D-LBT的高光谱图像压缩方法研究及硬件实现

基于3D-LBT的高光谱图像压缩方法研究及硬件实现

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作者：郭新雨哈尔滨工业大学

学位级别：硕士

近年来,高光谱传感器分辨率的不断提升致使高光谱图像数据量显著增加,使高光谱图像压缩变得越来越关键和必要。其中,基于变换的高光谱图像压缩方法是典型方法之一,具有有损无损兼容、有利于硬件实现等特点。本文研究并改进了一种基于3D-... 详细信息

近年来,高光谱传感器分辨率的不断提升致使高光谱图像数据量显著增加,使高光谱图像压缩变得越来越关键和必要。其中,基于变换的高光谱图像压缩方法是典型方法之一,具有有损无损兼容、有利于硬件实现等特点。本文研究并改进了一种基于3D-LBT(Three Dimension-Lapped Biorthogonal Transform)的无损高光谱压缩算法并对其进行了软件实现,并对关键模块进行了硬件实现。首先,基于3D-LBT设计了高光谱压缩算法,包括3D-LBT、预测算法以及编码器部分。针对预测部分,在研究了3D-LBT变换后高频系数、低频系数以及直流系数三种系数的相关性的基础上,有针对性地改进了具有三种模式的差分脉冲编码调制(Differential Pulse Code Modulation,DPCM)算法。对于熵编码部分,优化设计了range coder的基本理论以及其技术实现方法,在编码器之前Histgram Packing以衔接预测运算与编码器,并且为了进一步改善编码效果在编码器之前增加了Positive Mapping运算,之后对于range coder内部的累计频度统计的算法进行了改进。然后在基于Visual Studio 2012平台实现了该算法,并以Yellowstone calibrated scene 0、3、10三幅高光谱图像进行了验证,得到平均压缩比为3.603。其次,采用超高速集成电路硬件描述语言(VHDL)实现了压缩算法中的预测和编码模块,并在其内部使用流水线结构将各子模块组织起来,使用Modelsim10.1d对所编的模块进行寄存器传输级(Register-Transfer Level,RTL)仿真,并在对其进行综合之后进行门级仿真,以为各模块之上的流水线结构实现做好铺垫。并按照流水线结构将各模块组织起来,在利用Quartus II对其进行综合之后,使用Modelsim10.1d对其进行RTL以及门级仿真,使用截取后的Yellowstone calibrated scene 0、3、10三幅高光谱图像经软件变换后所得的变换系数作为数据源对其进行了验证,达到了268.1Mbps的吞吐率,并将得到的结果与软件所得结果相比较,得到了相同的结果。最后,在基于EP2S130F1020I4的FPGA硬件平台上以100MHz的时钟验证了本文算法中的编码器模块,得到了与仿真以及软件系统相同的结果,验证了本文所实现硬件系统的有效性。

关键词：高光谱图像无损压缩预测 range coder FPGA

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