This paper presents a novel image coding scheme based on optimal filter design for subband coding followed by vector quantization. A generalized perfect reconstruction subband filter system is proposed in which constr...
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This paper presents a novel image coding scheme based on optimal filter design for subband coding followed by vector quantization. A generalized perfect reconstruction subband filter system is proposed in which constraints on filter coefficients have been removed so that optimal filter bank which is more effective for subband coding can be designed. A filter design criteria based on an upper bound on the entropy of the decomposed subimage is proposed. This upper bound is obtained by assuming that the probability density function of the pixel values of the image is Gaussian. An explicit function of this bound in terms of the filter response is derived. The optimal filter is designed by minimization of the bound with respect to the filter response using the steepest descent algorithm. The decomposed subimages are coded using vector quantization with varying bit allocation strategy, followed by lossless coding for further data compression. A significant compression ratio of 42:1 with image quality superior to that by JPEG is achieved. Computer simulation results using real images are presented and compared with other existing algorithms.
The redundancy of the multiresolution representation has been clearly demonstrated in the case of fractal images, but it has not been fully recognized and exploited for general images. Recently, fractal block coders h...
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The redundancy of the multiresolution representation has been clearly demonstrated in the case of fractal images, but it has not been fully recognized and exploited for general images. Recently, fractal block coders have exploited the selfsimilarity among blocks in images. In this work, we devise an image coder in which the causal similarity among blocks of different subbands in a multiresolution decomposition of the image is exploited. In a pyramid subband decomposition, the image is decomposed into a set of subbands that are localized in scale, orientation, and space. The proposed coding scheme consists of predicting blocks in one subimage from blocks in lower resolution subbands with the same orientation. Although our prediction maps are of the same kind of those used in fractal block coders, which are based on an iterative mapping scheme, our coding technique does not impose any contractivity constraint on the block maps, This makes the decoding procedure very simple and allows a direct evaluation of the mean squared error (MSE) between the original and the reconstructed image at coding time. More importantly, we show that the subband pyramid acts as an automatic block classifier, thus making the block search simpler and the block matching more effective, These advantages are confirmed by the experimental results, which show that the performance of our scheme is superior for both visual quality and MSE to that obtainable with standard fractal block coders and also to that of other popular image coders such as JPEG.
A high-compression image coding scheme is presented, based on thread-like ''Ridge'' and ''Valley'' primitives, The use of these primitives is motivated by their success in economically ...
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A high-compression image coding scheme is presented, based on thread-like ''Ridge'' and ''Valley'' primitives, The use of these primitives is motivated by their success in economically representing image structure, The original image is sampled along the primitives, using a fractal yardstick method to determine sample spacing, The primitives themselves are compressed using vector coding and chain coding, Reconstruction at the receiver is a scattered data interpolation problem, solved here using C-0 Natural Neighbor Interpolation. Results are presented showing data rates between 0.1 and 0.4 b/pixel, the degradations are discussed, and prospects for improvement outlined.
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.
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