In this paper, an improved wavelet compression algorithm is proposed for electrocardiogram (ECC) signals which is combined with the lifting wavelet transform (WT) and the dynamic multi-stage vector quantization (MS-VQ...
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ISBN:
(纸本)9781424427994
In this paper, an improved wavelet compression algorithm is proposed for electrocardiogram (ECC) signals which is combined with the lifting wavelet transform (WT) and the dynamic multi-stage vector quantization (MS-VQ). The lifting wavelet transformed coefficients in a hierarchical tree order are taken as the components of a vector called a tree vector (TV). Based on the property of wavelet coefficients that gives emphasis to approximation coefficients, the components of a target vector for two stages VQ is differently extracted front different WT sub-bands. In the first-stage, 32 dimensional TV for crude quantization is extracted in the order of a hierarchical tree from a WT sub-bands except the last sub-band and in the second stage, 64 dimensional code vectors from all WT sub-bands. Each codebook is adaptively updated by the distortion constrained codebook replenishment mechanism (DCCR). The combination of lifting WT and dynamic multi-stage VQ retains feature integrity of the ECG at high compression ratios. Preliminary results indicate that the proposed method excels over previous techniques for high fidelity compression.
Spectral envelope modeling is an instrumental part of speech and audio codecs, which can be used to enable efficient entropy coding of spectral components. Overall optimization of codecs, including envelope models, ha...
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ISBN:
(纸本)9781713836902
Spectral envelope modeling is an instrumental part of speech and audio codecs, which can be used to enable efficient entropy coding of spectral components. Overall optimization of codecs, including envelope models, has however been difficult due to the complicated interactions between different modules of the codec. In this paper, we study an end-to-end optimization methodology to optimize all modules in a codec integrally with respect to each other while capturing all these complex interactions with a global loss function. For the quantization of the spectral envelope parameters with a fixed bitrate, we use multistagevectorquantization which gives high quality, but yet has a computational complexity which can be realistically applied in embedded devices. The obtained results demonstrate benefits in terms of PESQ and PSNR in comparison to the 3GPP EVS, as well as our recently proposed PyAWNeS codecs.
In this paper, an improved wavelet compression algorithm is proposed for electrocardiogram (ECG) signals which is combined with the lifting wavelet transform (WT) and the dynamic multi-stage vector quantization (MS-VQ...
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ISBN:
(纸本)9781424427994
In this paper, an improved wavelet compression algorithm is proposed for electrocardiogram (ECG) signals which is combined with the lifting wavelet transform (WT) and the dynamic multi-stage vector quantization (MS-VQ). The lifting wavelet transformed coefficients in a hierarchical tree order are taken as the components of a vector called a tree vector (TV). Based on the property of wavelet coefficients that gives emphasis to approximation coefficients, the components of a target vector for two stages VQ is differently extracted from different WT sub-bands. In the first-stage, 32 dimensional TV for crude quantization is extracted in the order of a hierarchical tree from a WT sub-bands except the last sub-band and in the second stage, 64 dimensional code vectors from all WT sub-bands. Each codebook is adaptively updated by the distortion constrained codebook replenishment mechanism (DCCR). The combination of lifting WT and dynamic multi-stage VQ retains feature integrity of the ECG at high compression ratios. Preliminary results indicate that the proposed method excels over previous techniques for high fidelity compression.
We study joint source-channel coding (JSCC) of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a framework for realizing optimum JSCC schemes that enable encoding and transmitting CS measu...
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We study joint source-channel coding (JSCC) of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a framework for realizing optimum JSCC schemes that enable encoding and transmitting CS measurements of a sparse source over discrete memoryless channels, and decoding the sparse source signal. For this purpose, the optimal design of encoder-decoder pair of a VQ is considered, where the optimality is addressed by minimizing end-to-end mean square error (MSE). We derive a theoretical lower bound on the MSE performance and propose a practical encoder-decoder design through an iterative algorithm. The resulting coding scheme is referred to as channel-optimized VQ for CS, coined COVQ-CS. In order to address the encoding complexity issue of the COVQ-CS, we propose to use a structured quantizer, namely low-complexity multistage VQ (MSVQ). We derive new encoding and decoding conditions for the MSVQ and then propose a practical encoder-decoder design algorithm referred to as channel-optimized MSVQ for CS, coined COMSVQ-CS. Through simulation studies, we compare the proposed schemes vis-a-vis relevant quantizers.
The distance measure is of great importance in both the design and coding stage of a vector quantizer. Due to its complexity, however, the spectral distance which best correlates with the perceptual quality is seldom ...
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The distance measure is of great importance in both the design and coding stage of a vector quantizer. Due to its complexity, however, the spectral distance which best correlates with the perceptual quality is seldom used. On the other hand, various weighted squared Euclidean distance measures give close or even accurate estimation of the meaningful spectral distance. Since they are in general mathematically more tractable, these weighted squared Euclidean distance measures are more commonly used. Significant differences can be found in the performance of different distance measures suggested in previous literatures. In this paper, a complete study and comparison of weighted squared Euclidean distance measures is given. This paper also proposes a new weighted squared Euclidean distance measure for vectorquantization of Line Spectrum Pairs (LSP) or Cosine of LSP (CLSP) parameters. It also presents an efficient adaptation apparatus for using the proposed distance measure in the case of split or multi-stagevector quantizers. (C) 1999 Elsevier Science B.V. All rights reserved.
Perceptual image hashing has become an emerging solution for image indexing, authentication and watermarking. A perceptual image hash function maps a digital image into a fixed length binary string known as the hash v...
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Perceptual image hashing has become an emerging solution for image indexing, authentication and watermarking. A perceptual image hash function maps a digital image into a fixed length binary string known as the hash value, which is invariant under changes to the image that is perceptually insignificant. In this paper, a multipurpose image hashing scheme based on multi-stage vector quantization (MSVQ) is proposed for both copyright protection and content authentication. The original gray-level image is first segmented into non-overlapping blocks. Each block is encoded by a two-stagevector quantizer to generate two indices, one for copyright protection and the other for content authentication. The obtained two index tables are then transformed into two binary images based on specific mapping functions. Finally, the authentication mark and permuted copyright logo are respectively XOR-ed with the two binary images to obtain final authentication and protection fingerprints. Compared with the existing DCTVQ based multipurpose hashing scheme, we provide two new mapping functions and our hashing process is much faster. Experimental results show the effectiveness of the proposed method.
作者:
Liang, YanxiaYang, JiaweiLi, YeXidian Univ
Inst Informat Sci Broadband Wireless Commun Lab State Key Lab Integrated Serv Network Xian 710071 Shaanxi Peoples R China
This paper presents a multimode multi-Band Excitation with Linear Prediction model (MMBE-LP) at 2.35kbps. Unvoiced/Voiced (U/V) decisions and spectrum amplitudes estimations are improved in this vocoder compared with ...
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ISBN:
(纸本)9781424458516
This paper presents a multimode multi-Band Excitation with Linear Prediction model (MMBE-LP) at 2.35kbps. Unvoiced/Voiced (U/V) decisions and spectrum amplitudes estimations are improved in this vocoder compared with MBE vocoder. For better quantization results, different codebooks are used for different modes of U/V decision, so the number of sub-bands in a frame is fixed. Spectral amplitudes are estimated by Linear Prediction Mode and quantized by MSVQ (multi-stage vector quantization). Simulation results show that the unvoiced and voiced parts of synthetic speech are coherent with the parts of original speech obviously.
This paper presents a multimode multi-Band Excitation with Linear Prediction model (MMBE-LP) at 2.35kbps. Unvoiced/Voiced (U/V) decisions and spectrum amplitudes estimations are improved in this vocoder compared with ...
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ISBN:
(纸本)9781424458509;9781424458493
This paper presents a multimode multi-Band Excitation with Linear Prediction model (MMBE-LP) at 2.35kbps. Unvoiced/Voiced (U/V) decisions and spectrum amplitudes estimations are improved in this vocoder compared with MBE vocoder. For better quantization results, different codebooks are used for different modes of U/V decision, so the number of sub-bands in a frame is fixed. Spectral amplitudes are estimated by Linear Prediction Mode and quantized by MSVQ (multi-stage vector quantization). Simulation results show that the unvoiced and voiced parts of synthetic speech are coherent with the parts of original speech obviously.
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