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Lossy Cutset coding of Bilevel Images Based on Markov Random Fields

IEEE TRANSACTIONS ON IMAGE PROCESSING 2014年第4期23卷 1652-1665页

作者： Reyes, Matthew G. Neuhoff, David L. Pappas, Thrasyvoulos N. Univ Michigan Dept Elect & Engn Comp Sci Ann Arbor MI 48109 USA Northwestern Univ Dept Elect & Engn Comp Sci Evanston IL 60208 USA

An effective, low complexity method for lossy compression of scenic bilevel images, called lossy cutset coding, is proposed based on a Markov random field model. It operates by losslessly encoding pixels in a square grid of lines, which is a cutset with respect to a Markov random field model, and preserves key structural information, such as borders between black and white regions. Relying on the Markov random field model, the decoder takes a MAP approach to reconstructing the interior of each grid block from the pixels on its boundary, thereby creating a piecewise smooth image that is consistent with the encoded grid pixels. The MAP rule, which reduces to finding the block interiors with fewest black-white transitions, is directly implementable for the most commonly occurring block boundaries, thereby avoiding the need for brute force or iterative solutions. Experimental results demonstrate that the new method is computationally simple, outperforms the current lossy compression technique most suited to scenic bilevel images, and provides substantially lower rates than lossless techniques, e.g., JBIG, with little loss in perceived image quality.

关键词： Lossy bilevel image coding lossy bilevel image compression arithmetic coding Ising model odd bonds Markov random fields MAP image reconstruction image interpolation

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Patch-Based Conditional Context coding of Stereo Disparity Images

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IEEE SIGNAL PROCESSING LETTERS 2014年第10期21卷 1220-1224页

作者： Tabus, Ioan Tampere Univ Technol Dept Signal Proc FIN-33101 Tampere Finland

This letter proposes a method for lossless coding the left disparity image, L, from a stereo disparity image pair (L, R), conditional on the right disparity image, R, by keeping track of the transformation of the constant patches from R to L. The disparities in R are used for predicting the disparities in L, and the locations of the pixels where the prediction is erroneous are encoded in a first stage, conditional on the patch-labels of R image, allowing the decoder to already reconstruct with certainty some elements of the L image, e.g., the disparity values at certain pixels and parts of the contours of left image patches. Second, the contours of the patches in L image that are still unknown after first stage are conditionally encoded using a mixed conditioning context: the usual causal current context from the contours of L and a noncausal context extracted from the contours in the correctly estimated part of L obtained in the first stage. The depth values in the patches of L image are finally encoded, if they are not already known from the prediction stage. The new algorithm, dubbed conditional crack-edge region value (C-CERV), is shown to perform significantly better than the non-conditional coding method CERV and than another existing conditional coding method, over the Middlebury corpus. C-CERV is shown to reach lossless compression ratios of 100-250 times for those images that have a high precision of the disparity map.

关键词： arithmetic coding context tree coding inter-coding lossless disparity image compression

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Adaptive CU splitting and pruning method for HEVC intra coding

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ELECTRONICS LETTERS 2014年第10期50卷 748-U131页

作者： Kim, Gaewon Yim, Changhoon Konkuk Univ Dept Internet & Multimedia Engn Seoul South Korea

coding unit (CU) splitting and pruning for complexity reduction in high-efficiency video coding (HEVC) intra coding is dealt with. Adaptive determination of the threshold values for splitting and pruning in each CU is proposed based on the depth level information of neighbour CUs, whose values are determined from a fixed parameter in the previous method. Simple preconditions for splitting and pruning are also proposed to improve coding efficiency. Simulation results show that the proposed method gives significant improvement of computational complexity with much smaller reduction of coding efficiency compared with the previous method.

关键词： VIDEO coding (Computer science) arithmetic coding SPLITTING extrapolation method SIMULATION methods & models COMPUTATIONAL complexity

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Low-Complexity Lossless Image coding

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2014年第5期48卷 303-311页

作者： Bocharova, I. E. Porov, A. V. Bondarev, T. S. Finkelshteyn, O. V. Natl Res Univ Informat Technol Mech & Opt Kronverksky Prosp 49 St Petersburg 197101 Russia

A new lossless image coding method competitive with the best known image coding techniques in terms of efficiency and complexity is suggested. It is based on adaptive color space transform, adaptive context coding, and improved prediction of pixel values of image color components. Examples of application of the new algorithm to a set of standard images are given and comparison with known algorithms is performed.

关键词： lossless compression linear prediction image coding context coding color space transform arithmetic coding

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LOSSLESS AUDIO COMPRESSION IN THE NEW IEEE STANDARD FOR ADVANCED AUDIO coding

LOSSLESS AUDIO COMPRESSION IN THE NEW IEEE STANDARD FOR ADVA...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Huang, H. Shu, H. Yu, R. ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781479928934

The IEEE Standard for Advanced Audio coding (IEEE 1857.2) is a new standard approved by IEEE in August 2013. The standard comprises both lossy and lossless audio compression tools. This paper presents the lossless audio compression tool, which utilizes a pre-processing procedure for flattening the amplitude envelop of linear prediction residue, and an arithmetic coder that adopts a scaled probability template. The performance of the new IEEE lossless compressor is evaluated and compared with state-of-the-art lossless audio coders. Evaluation results show that the lossless compression performance of the IEEE compressor is about 5% higher than MPEG-4 ALS and 12% higher than FLAC.

关键词： Lossless audio compression IEEE 1857.2 linear prediction arithmetic coding

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Burrows-Wheeler Transform and Combination of Move-to-Front coding and Run Length Encoding for Lossless Audio coding 9

Burrows-Wheeler Transform and Combination of Move-to-Front C...

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9th International Conference on Computer Engineering & Systems (ICCES)

作者： Elsayed, Hend. A. Delta Univ Sci & Technol Fac Engn Dept Commun & Comp Engn Mansoura Egypt

ISBN: (纸本)9781479965946

This paper presents a lossless audio coding using Burrows-Wheeler Transform (BWT) and a combination of a Move-To-Front coding (MTF) and Run Length Encoding (RLE). Audio signals used are assumed to be of floating point values. The BWT is applied to this floating point values to get the transformed coefficients;and then these resulting coefficients are converted using the Move-to-Front coding to coefficients can be better compressed and then these resulting coefficients are compressed using a combination of the Run Length Encoding, and entropy coding. Two entropy coding are used which are arithmetic and Huffman coding. Simulation results show that the proposed lossless audio coding method outperforms other lossless audio coding methods;using only Burrows-Wheeler Transform method, using combined Burrows-Wheeler Transform and Move-to-Front coding method, and using combined Burrows-Wheeler Transform and Run Length Encoding method.

关键词： Audio coding Burrows-Wheeler Transform Move-To-Front coding Run Length Encoding Entropy coding arithmetic coding Huffman coding

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High-Throughput and Low-Complexity Binary arithmetic Decoder based on Logarithmic Domain

High-Throughput and Low-Complexity Binary Arithmetic Decoder...

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IEEE International Conference on Image Processing

作者： Quanhe Yu Xiaozhen Zheng Jianhua Zheng Yun He Wei Yu Dadong Wang Junyou Chen Yangyang Xu Tsinghua National Laboratory for Information Science and Technology Department of Electronic Engineering Tsinghua University Research Department HiSilicon Technologies

ISBN: (纸本)9781479983407

This paper proposes a low-complexity and high-throughput decoder (D_LBAC) based on Logarithmic Binary arithmetic coding (LBAC). The proposed D_LBAC has high throughput and low complexity. It does not use multiplication and division operations nor look up tables (LUTs). The proposed D_LBAC is a simple algorithm structure and only requires additions and shift operations. Experimental results show it can decode 3.5 symbols per cycle on average. The hardware implementation design described in this paper can achieve the high symbol processing capability and the low hardware costs.

关键词： arithmetic coding LBAC H.265/HEVC CABAC Entropy coding

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Lossless Compression on MRI Images Using SWT

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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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NEW BIT-PLANE PROBABILITY CALCULATIONS FOR SCALABLE TO LOSSLESS AUDIO coding

NEW BIT-PLANE PROBABILITY CALCULATIONS FOR SCALABLE TO LOSSL...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Movassagh, Mahmood Kabal, Peter McGill Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9781479928934

Considering the properties of the residual signal, core-based bit-plane probabilities are provided for MPEG-4 Audio Scalable to Lossless coding (SLS), which matches the quantization and coding performed in the core layer. Using the same strategy, new probabilities are obtained to consider the clipping effect in bit-plane coding of an unbounded signal, which is useful for non-core mode of SLS coding. Simulations show that considering the core layer parameters and the clipping effect improve the bit-plane probabilities estimation compared to the existing method.

关键词： Scalable Audio coding Quantization Bit-Plane coding arithmetic coding AAC SLS

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