This paper proposes an efficient compression scheme for compressing time-varying medical volumetric data. The scheme uses 3-D motion estimation to create a homogenous preprocessed data to be compressed by a 3-D image ...
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This paper proposes an efficient compression scheme for compressing time-varying medical volumetric data. The scheme uses 3-D motion estimation to create a homogenous preprocessed data to be compressed by a 3-D image compression algorithm using hierarchical vector quantization. A new block distortion measure, called variance of residual (VOR), and three 3-D fast block matching algorithms are used to improve the motion estimation process in term of speed and data fidelity. The 3-D image compression process involves the application of two different encoding techniques based on the homogeneity of input data. Our method can achieve a higher fidelity and faster decompression time compared to other lossy compression methods producing similar compression ratios. The combination of 3-D motion estimation using VOR and hierarchical vector quantization contributes to the good performance. (C) 2011 Elsevier Ltd. All rights reserved.
We derive a JPEG compliant image compressor, which is based on hierarchical vector quantization (HVQ). The goal is to reduce complexity while increasing compression speed, For each block, the DCT IDC coefficient is en...
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We derive a JPEG compliant image compressor, which is based on hierarchical vector quantization (HVQ). The goal is to reduce complexity while increasing compression speed, For each block, the DCT IDC coefficient is encoded in the regular way, while the residual is mapped through HVQ to a precomputed bit stream corresponding to the compressed DCT AC coefficients. Approximation quality is generally good for high compression ratios. Color Fax is one possible application target for the proposed system.
VQ(vectorquantization) reduce the bit rate by exploting the correlation in the data. To improve the performance of a compression algorithm based on VQ, this paper introduced a more efficient scanning method, i.e. Pea...
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ISBN:
(纸本)0819430064
VQ(vectorquantization) reduce the bit rate by exploting the correlation in the data. To improve the performance of a compression algorithm based on VQ, this paper introduced a more efficient scanning method, i.e. Peanoscanning, which maintains better correlation in two dimensional data than that of raster scan, and then a hierarchical VQ based on the characteristics of image data is presented, at last we reduced the blocking effect by a smoothing algorithm.
We introduce a method to perform filtering on approximations (quantized versions) of the input signal, which lends itself to a practical implementation solely based on look-up tables (LUTs). The LUT filter approximate...
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We introduce a method to perform filtering on approximations (quantized versions) of the input signal, which lends itself to a practical implementation solely based on look-up tables (LUTs). The LUT filter approximates the performance of some traditional nonlinear filters at a traction of the cost. The filter is divided into an approximation stage that is constant for all filters and a filtering stage, which is one LUT that can change in order to implement different filters. We introduce an overlapped hierarchical vector quantization (OHVQ) scheme that is used as the approximation stage. The output is produced by mapping the OHVQ codes to filtered data. Hence, all processing is done via LUTs, even though the filter size needs to be small because of typical OHVQ contraints. Switching among filters demands changing pointers to only one small LUT. Preliminary analysis and image processing examples are shown, demonstrating the efficacy of the proposed method.
To be able to compact large amounts of multimedia data and route it through a busy network at interactive rates has emerged as one of the biggest technological challenges of our times. Recently, there has been much ac...
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ISBN:
(纸本)0819427497
To be able to compact large amounts of multimedia data and route it through a busy network at interactive rates has emerged as one of the biggest technological challenges of our times. Recently, there has been much activity in the areas of theoretical compression models using wavelets(4,19,20), evaluation of suitable wavelets for compression(1,2), fast real-time compression/decompression systems(3,23) and parallelized VLSI algorithms(10,21), Little work has been done towards integrating these developments into a tightly coupled and highly optimized scheme. We take a unified approach to developing a real-time compression/transmission system using a tight coupling of hierarchical vector quantization (HVQ) on discrete wavelet transformed (DWT) images. We simultaneously optimize for speed, performance and scalability on several fronts, e.g. choice of wavelet, parallelizability, and efficient VLSI implementation. In doing so we demonstrate a speedup of O(logL) (L = length of wavelet filter)(3), as well as reduce storage by a factor of O(logL)(3). To achieve this we argue that the simplest wavelets, i.e. the Haar bases suffice for our scheme, because HVQ retains detail coefficients. We also show how to integrate the algorithm into the parallel graphics library (PGL), in order to achieve parallelized compression and progressive transmission of images.
We introduce two suboptimal search heuristics for reducing the computational burden in single channel source separation. The heuristics approximating the observation likelihood are evaluated using the speaker dependen...
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ISBN:
(纸本)9781424442966
We introduce two suboptimal search heuristics for reducing the computational burden in single channel source separation. The heuristics approximating the observation likelihood are evaluated using the speaker dependent factorial-max VQ model. One approach extends beam search, whereas the second relies on the iterated conditional modes algorithm. We compare the methods to the hierarchically structured VQ model [1] and to the full search using the Grid Corpus [2]. The first two algorithms reduce the computational costs by almost two orders of magnitude compared to full search, whereas the separation performance shows a slight and insignificant decrease in terms of target-to-masker ratio. Additionally, the heuristics are compared in terms of execution time.
We introduce two suboptimal search heuristics for reducing the computational burden in single channel source separation. The heuristics approximating the observation likelihood are evaluated using the speaker dependen...
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ISBN:
(纸本)9781424442959;9781424442966
We introduce two suboptimal search heuristics for reducing the computational burden in single channel source separation. The heuristics approximating the observation likelihood are evaluated using the speaker dependent factorial-max VQ model. One approach extends beam search, whereas the second relies on the iterated conditional modes algorithm. We compare the methods to the hierarchically structured VQ model [1] and to the full search using the Grid Corpus [2]. The first two algorithms reduce the computational costs by almost two orders of magnitude compared to full search, whereas the separation performance shows a slight and insignificant decrease in terms of target-to-masker ratio. Additionally, the heuristics are compared in terms of execution time.
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