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 g...
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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.
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 cons...
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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.
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...
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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.
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, an...
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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.
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 aud...
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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.
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...
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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.
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 multiplicatio...
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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.
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 per...
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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.
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 Spectromet...
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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
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 la...
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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.
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