New lattice vector quantizer design procedures for nonuniform sources that yield excellent performance while retaining the structure required for fast quantization are described. Analytical methods for truncating and ...
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New lattice vector quantizer design procedures for nonuniform sources that yield excellent performance while retaining the structure required for fast quantization are described. Analytical methods for truncating and scaling lattices to be used in vector quantization are given, and an analytical technique for piecewise-linear multidimensional companding is presented, The uniform and piecewise-uniform lattice vector quantizers are then used to quantize the discrete cosine transform coefficients of images, and their objective and subjective performance and complexity are contrasted with other lattice vector quantizers and with LBG training-mode designs.
In this paper, a new family of multiband wavelets with a parameter lambda is introduced for image coding. In our method of image coding, subbands in the wavelet decomposition are adaptively divided into insignificant ...
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In this paper, a new family of multiband wavelets with a parameter lambda is introduced for image coding. In our method of image coding, subbands in the wavelet decomposition are adaptively divided into insignificant subbands and significant subbands while the latter are further partitioned by a significance benchmark and by the quad-tree partition algorithm. Our experimental results show less computational cost and better capability for our method than those based on two-band wavelets. (c) 2005 Elsevier B.V. All rights reserved.
In this paper, we introduce a new image compression scheme that it involves three steps: First a multiresolution decomposition of the images is performed using the Wavelet Transform (WT). PL thresholding algorithm is ...
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In this paper, we introduce a new image compression scheme that it involves three steps: First a multiresolution decomposition of the images is performed using the Wavelet Transform (WT). PL thresholding algorithm is then used for the wavelet coefficients. Finally, the coefficients derived from the second step are vector quantized using a multiresolution codebook. The LGB algorithm is used for the Vector Quantization (VQ). Our experimental results showed that the Lena image can be coded by a two-level system at the rate of 0.24 bpp having a PSNR of 30.40 db.
The classified side-match vector quantizer, CSMVQ, has already been presented for low-bit-rate image encoding. It exploits a block classifier to decide which class the input vector belongs to using the variances of th...
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The classified side-match vector quantizer, CSMVQ, has already been presented for low-bit-rate image encoding. It exploits a block classifier to decide which class the input vector belongs to using the variances of the upper and left codewords. However, this block classifier doesn't take the variance of the current input vector itself into account. This letter presents a new CSMVQ in which a two-level block classifier is used to classify input vectors and two different master codebooks are used for generating the state codebook according to the variance of the input vector. Experimental results prove the effectiveness of the proposed CSMVQ.
In the future, B-ISDN (Broad-band Integrated Services Digital Network) users can send various kinds of information, such as voice, data, and image, over the same network and send information only when necessary. It ha...
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In the future, B-ISDN (Broad-band Integrated Services Digital Network) users can send various kinds of information, such as voice, data, and image, over the same network and send information only when necessary. It has been recognized that variable-rate encoding techniques are more suitable than fixed-rate techniques for encodingimages in a B-ISDN environment. This paper describes a new variable-rate side-match finite-state vector quantization with a block classifier (CSMVQ) algorithm. In an ordinary fixed-rate SMVQ, the size of the state codebook is fixed. In our CSMVQ algorithm, the size of the state codebook is changed according to the characteristics of the current vector which can be predicted by a block classifier. In our experiments, the improvement over SMVQ is up to 1.761 dB at a lower bit rate. Moreover, the improvement over VQ can be up to 3 dB at nearly the same bit rate.
This paper defines a discrete-time wavelet transform absolute maxima (DWTAM) representation for images and presents the corresponding image recovery algorithm from the representation to compress the image data. The co...
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This paper defines a discrete-time wavelet transform absolute maxima (DWTAM) representation for images and presents the corresponding image recovery algorithm from the representation to compress the image data. The compression algorithm is developed by using the method of convex projection and the entropy coding technique. (C) 1999 Elsevier Science B.V. All rights reserved.
In this letter, a vector quantization (VQ) scheme with finite memory called feature map finite-state vector quantization (FMFSVQ) is presented, The FMFSVQ takes advantage of good topological ordering so that the desig...
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In this letter, a vector quantization (VQ) scheme with finite memory called feature map finite-state vector quantization (FMFSVQ) is presented, The FMFSVQ takes advantage of good topological ordering so that the design of state codebooks is simplified, Our FMFSVQ also has no duplication of state codebooks, no synchronization required between encoder and decoder, and a very simple decoder, An adaptive FMFSVQ scheme is also proposed, Experimental results are presented for different super codebook sizes and different state codebook sizes.
Classified transform coding of images using vector quantization (VQ) has proved to be an efficient technique. Transform VQ combines the energy compaction properties of transform coding and the superior performance of ...
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Classified transform coding of images using vector quantization (VQ) has proved to be an efficient technique. Transform VQ combines the energy compaction properties of transform coding and the superior performance of VQ. Classification improves the reconstructed image quality considerably because of adaptive bit allocation. In this paper, a classified transform VQ technique using the lapped orthogonal transform (LOT) is presented. image blocks are transformed using the LOT and are classified into four classes based on their structural properties. These are further divided adaptively into subvectors depending on the LOT coefficient statistics as this allows efficient distribution of bits. These subvectors are then vector quantized. Simulation results indicate subjectively improved images with LOT/VQ as compared to DCT/VQ.
作者：
Watanabe, TLaboratory of Chromatography
DEPg.Fac.Quimica Universidad Nacional Autonoma de Mexico Circuito interior Cd Universitaria/CP 04510 Mexico D.F.Mexico
This paper presents a new coding algorithm making use of B-spline surfaces, Points on the surface are regarded as image data, and the values of defining polygon vertices are coded. Simulation results show the possibil...
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This paper presents a new coding algorithm making use of B-spline surfaces, Points on the surface are regarded as image data, and the values of defining polygon vertices are coded. Simulation results show the possibility that the performance of this algorithm becomes better than that of discrete cosine transform (DCT) when parameter values of the surface are set according to the position and the steepness of edges, and the number of polygon vertices is set according to the fineness of the image and the bitrate. It is known that a B-spline surface is transformed by transforming the defining polygon net, Therefore, using this algorithm, it becomes possible to apply image transformations to compressed data (polygon vertices) instead of to decoded data.
The feasibility of coding two-dimensional data arrays by first performing a two-dimensional linear transformation on the data and then block quantizing the transformed data is investigated. The Fourier, Hadamard, and ...
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The feasibility of coding two-dimensional data arrays by first performing a two-dimensional linear transformation on the data and then block quantizing the transformed data is investigated. The Fourier, Hadamard, and Karhunen-Loève transformations are considered. Theoretical results for Markov data and experimental results for four pictures comparing these transform methods to the standard method of raster scanning, sampling, and pulse-count modulation code are presented.
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