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Lossless Compression on MRI Images Using SWT

JOURNAL OF DIGITAL IMAGING 2014年第5期27卷 594-600页

作者： Anusuya, V. Raghavan, V. Srinivasa Kavitha, G. PSR Engn Coll Dept Comp Sci & Engn Sivakasi India Loyola Inst Technol Chennai Tamil Nadu India AAA Coll Engn & Technol Sivakasi Tamil Nadu India

Medical image compression is one of the growing research fields in biomedical applications. Most medical images need to be compressed using lossless compression as each pixel information is valuable. With the wide pervasiveness of medical imaging applications in health-care settings and the increased interest in telemedicine technologies, it has become essential to reduce both storage and transmission bandwidth requirements needed for archival and communication of related data, preferably by employing lossless compression methods. Furthermore, providing random access as well as resolution and quality scalability to the compressed data has become of great utility. Random access refers to the ability to decode any section of the compressed image without having to decode the entire data set. The system proposes to implement a lossless codec using an entropy coder. 3D medical images are decomposed into 2D slices and subjected to 2D-stationary wavelet transform (SWT). The decimated coefficients are compressed in parallel using embedded block coding with optimized truncation of the embedded bit stream. These bit streams are decoded and reconstructed using inverse SWT. Finally, the compression ratio (CR) is evaluated to prove the efficiency of the proposal. As an enhancement, the proposed system concentrates on minimizing the computation time by introducing parallel computing on the arithmetic coding stage as it deals with multiple subslices.

关键词： Stationary wavelet transform EBCOT arithmetic coding Bit plane coding

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FIVQ algorithm for interference hyper-spectral image compression

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OPTICS COMMUNICATIONS 2014年 322卷 97-104页

作者： Wen, Jia Ma, Caiwen Zhao, Junsuo Chinese Acad Sci Sci Integrated Informat Syst Lab Inst Software Beijing 100190 Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Shaanxi Peoples R China

Based on the improved vector quantization (IVQ) algorithm [1] which was proposed in 2012, this paper proposes a further improved vector quantization (FIVQ) algorithm for LASIS (Large Aperture Static Imaging Spectrometer) interference hyper-spectral image compression. To get better image quality, IVQ algorithm takes both the mean values and the VQ indices as the encoding rules. Although IVQ algorithm can improve both the bit rate and the image quality, it still can be further improved in order to get much lower bit rate for the LASlS interference pattern with the special optical characteristics based on the pushing and sweeping in LASIS imaging principle. In the proposed algorithm FIVQ, the neighborhood of the encoding blocks of the interference pattern image, which are using the mean value rules, will be checked whether they have the same mean value as the current processing block. Experiments show the proposed algorithm FIVQ can get lower bit rate compared to that of the IVQ algorithm for the LASIS interference hyper-spectral sequences. (C) 2014 Elsevier B.V. All rights reserved

关键词： Image compression Vector quantization LBG algorithm arithmetic coding

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Asymptotically Optimum Perfect Universal Steganography of Finite Memoryless Sources

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IEEE TRANSACTIONS ON INFORMATION THEORY 2018年第2期64卷 1199-1216页

作者： Balado, Felix Haughton, David Univ Coll Dublin Sch Comp Sci Dublin 4 Ireland Ctr Appl Data Analyt Res Belfield Off Pk Dublin 4 Ireland

A solution to the problem of asymptotically optimum perfect universal steganography of finite memoryless sources with a passive warden is provided, which is then extended to contemplate a distortion constraint. The solution rests on the fact that Slepian's Variant I permutation coding implements first-order perfect universal steganography of finite host signals with optimum embedding rate. The duality between perfect universal steganography with asymptotically optimum embedding rate and lossless universal source coding with asymptotically optimum compression rate is evinced in practice by showing that permutation coding can be implemented by means of adaptive arithmetic coding. Next, a distortion constraint between the host signal and the information-carrying signal is considered. Such a constraint is essential whenever real-world host signals with memory (e.g., images, audio, or video) are decorrelated to conform to the memoryless assumption. The constrained version of the problem requires trading off embedding rate and distortion. Partitioned permutation coding is shown to be a practical way to implement this trade-off, performing close to an unattainable upper bound on the rate-distortion function of the problem.

关键词： Steganography source coding permutation coding arithmetic coding rate-distortion

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A two-dimensional approach for lossless EEG compression

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2011年第4期6卷 387-394页

作者： Srinivasan, K. Dauwels, Justin Reddy, M. Ramasubba Indian Inst Technol Dept Appl Mech Biomed Engn Grp Madras 600036 Tamil Nadu India Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this paper, we study various lossless compression techniques for electroencephalograph (EEG) signals. We discuss a computationally simple pre-processing technique, where EEG signal is arranged in the form of a matrix (2-D) before compression. We discuss a two-stage coder to compress the EEG matrix, with a lossy coding layer (SPIHT) and residual coding layer (arithmetic coding). This coder is optimally tuned to utilize the source memory and the lid. nature of the residual. We also investigate and compare EEG compression with other schemes such as JPEG2000 image compression standard, predictive coding based shorten, and simple entropy coding. The compression algorithms are tested with University of Bonn database and Physiobank Motor/Mental Imagery database. 2-D based compression schemes yielded higher lossless compression compared to the standard vector-based compression, predictive and entropy coding schemes. The use of pre-processing technique resulted in 6% improvement, and the two-stage coder yielded a further improvement of 3% in compression performance. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： arithmetic coding Correlation coefficient Electroencephalogram (EEG) JPEG2000 Relative energy concentration SPIHT

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Online text prediction with recurrent neural networks

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NEURAL PROCESSING LETTERS 2001年第2期14卷 127-140页

作者： Pérez-Ortiz, JA Calera-Rubio, J Forcada, ML Univ Alacant Dept Llenguatges & sistemes Informat E-03071 Alacant Spain

arithmetic coding is one of the most outstanding techniques for lossless data compression. It attains its good performance with the help of a probability model which indicates at each step the probability of occurrence of each possible input symbol given the current context. The better this model, the greater the compression ratio achieved. This work analyses the use of discrete-time recurrent neural networks and their capability for predicting the next symbol in a sequence in order to implement that model. The focus of this study is on online prediction, a task much harder than the classical offline grammatical inference with neural networks. The results obtained show that recurrent neural networks have no problem when the sequences come from the output of a finite-state machine, easily giving high compression ratios. When compressing real texts, however, the dynamics of the sequences seem to be too complex to be learned online correctly by the net.

关键词： arithmetic coding online nonlinear prediction recurrent neural networks text compression

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Error concealment for robust arithmetic decoding in mobile radio environments

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SIGNAL PROCESSING-IMAGE COMMUNICATION 1996年第5期8卷 411-419页

作者： Pelz, RM Jannet, B UNIV HANNOVER DEPT COMMUND-30167 HANNOVERGERMANY

arithmetic coding is a technique for achieving maximum compression in video codecs by exploiting any residual correlation exhibited by parameter sets delivered by the source coding algorithm. This inherent characteristic implies poor error resilience, specially in the interesting case of low bit-rate video transmission over mobile networks. In this paper, a modified arithmetic decoding algorithm based on the adaptation of the underlying model probabilities according to reliability information delivered by a soft output channel decoder is presented. Furthermore, additional techniques for enhancement of the concatenated channel-source decoding scheme based on spatial linear prediction in conjunction with reliability estimates of the received video frame are presented. The performance of the addressed error concealment techniques is evaluated and analyzed through simulations in the case of an error protected video transmission over a Gaussian and mobile Rayleigh fading channel.

关键词： image compression arithmetic coding error concealment reliability information mobile transmission

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Fault tolerance in computing, compressing, and transmitting FFT data

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IEEE TRANSACTIONS ON COMMUNICATIONS 2001年第12期49卷 2095-2105页

作者： Redinbo, GR Manomohan, R Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA Hewlett Packard Corp Internet Software Syst Engineer Cupertino CA 95014 USA

Remote-sensing applications often calculate the discrete Fourier transform of sampled data and then compress and encode it for transmission to a destination. However, all these operations are executed on computing resources potentially affected by failures. Methods are presented for integrating various fault detection capabilities throughout the data flow path so that the momentary failure of any subsystem will not allow contaminated data to go undetected. New techniques for protecting complete source coding schemes are exemplified by examining a lossy compression system that truncates fast Fourier transform (FFT) coefficients to zero, then compresses the data further by using lossless arithmetic coding. Novel methods protect arithmetic coding computations by internal algorithm checks. The arithmetic encoding and decoding operations and the transmission path are further protected by inserting sparse parity symbols dictated by a high-rate convolutional symbol-based code. This powerful approach introduces limited redundancy at the beginning of the system but performs detection at later stages. While the parity symbols degrade efficiency slightly, the overall compression gain is significant because of the run-length coding. Well-known fault tolerance measures for FFT algorithms are extended to detect errors in the lossy truncation operations, maintaining end-to-end protection. Simulations verify that all single subsystem errors are detected and the overhead costs are reasonable.

关键词： arithmetic coding fault detection FFT protection lossy compression protected source coding remote sensing run-length coding symbol convolutional codes

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Joint Algorithm-Architecture Optimization of CABAC

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2012年第3期69卷 239-252页

作者： Sze, Vivienne Chandrakasan, Anantha P. MIT Microsyst Technol Labs Cambridge MA 02139 USA

This paper uses joint algorithm and architecture design to enable high coding efficiency in conjunction with high processing speed and low area cost. Specifically, it presents several optimizations that can be performed on Context Adaptive Binary arithmetic coding (CABAC), a form of entropy coding used in H. 264/ AVC, to achieve the throughput necessary for real-time low power high definition video coding. The combination of syntax element partitions and interleaved entropy slices, referred to as Massively Parallel CABAC, increases the number of binary symbols that can be processed in a cycle. Subinterval reordering is used to reduce the cycle time required to process each binary symbol. Under common conditions using the JM12.0 software, the Massively Parallel CABAC, increases the bins per cycle by 2.7 to 32.8x at a cost of 0.25 to 6.84% coding loss compared with sequential single slice H. 264/ AVC CABAC. It also provides a 2x reduction in area cost, and reduces memory bandwidth. Subinterval reordering reduces the critical path delay by 14 to 22%, while modifications to context selection reduces the memory requirement by 67%. This work demonstrates that accounting for implementation cost during video coding algorithms design can enable higher processing speed and reduce hardware cost, while still delivering high coding efficiency in the next generation video coding standard.

关键词： CABAC arithmetic coding H.264/AVC Video coding Architecture Entropy coding

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Dynamic pipeline design of an adaptive binary arithmetic coder

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSING 2001年第9期48卷 813-825页

作者： Kuang, SR Jou, JM Chen, RD Shiau, YH So Taiwan Univ Technol Dept Elect Engn Tainan 70101 Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan

arithmetic coding is an attractive technique for lossless data compression but it tends to be slow. In this paper, a dynamic pipelined very large scale integration architecture with high performance for on-line adaptive binary arithmetic coding is presented. To obtain a high throughput pipelined architecture, we first analyze the computation flow of the coding algorithm and modify the operations whose data and/or control dependencies cause the difficulties in pipelining. Then, a novel technique called dynamic pipelining is developed to pipeline the coding process with variant (or run-time determined) pipeline latencies (or data initialization intervals) efficiently. As for data path design, a systematic design methodology of high level synthesis and a less-area but faster fixed-width multiplier are applied, which make the architecture with a little additional hardware. The dynamic pipelined architecture has been designed and simulated in Verilog HDL, and its layout has also been implemented with the 0.8-mum SPDM CMOS process and the ITRI-CCL cell library. Its simulated compression speeds under working frequencies of 25 and 50 MHz are about 6 and 12.5 Mb/s, respectively. About two times the speedup with 30% hardware overhead relative to the original sequential one is achieved.

关键词： arithmetic coding data compression dynamic pipeline

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Single-rate near lossless compression of animated geometry

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COMPUTER-AIDED DESIGN 2009年第10期41卷 711-718页

作者： Amjoun, Rachida Strasser, Wolfgang Univ Tubingen WSI GRIS Tubingen Germany

Representing mesh geometry in local rather than the world coordinate systems is very useful in many 3D animation processing applications. One can investigate the representation of vertex locations relative to a local coordinate frame (LCF) in the compression of dynamic 3D meshes. Unlike the world coordinates, which scatter in a wide range and show non-linear behavior of the vertices, the local coordinates exhibit a large clustering behavior of the vertex over time. This property is very useful for exploiting a large coherence over the vertex trajectory and between neighboring vertices. In this paper, we discuss the use of the LCF in static and animated mesh encoding and we introduce a new connectivity-guided predictive scheme for single-rate compression for animated meshes. The proposed geometry encoding strategy is based on a region growing encoding order, and only the differences between original and predicted locations are encoded in a local coordinate system, which splits into two tangential and one normal components. The approach is simple, efficient, and well-suited for real time applications. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： 3D mesh Animation Local coordinates Quantization arithmetic coding

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