Wavelet transforms have recently emerged as powerful tools for data compression in image and video applications. Based on the strong interscale correlation between different scales on the same orientation and location...
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Wavelet transforms have recently emerged as powerful tools for data compression in image and video applications. Based on the strong interscale correlation between different scales on the same orientation and location in wavelet domain, a vector quantization coding scheme with a hierarchical codebook in wavelet domain is developed in this paper. The structure of the hierarchical codebook is designed and its training and construction criterion, based upon the conditional probability concept, is also described. Finally, the simulation results are presented.
In this paper, an adaptive image coding technique-two-channel conjugate classified discrete cosine transform/vector quantization (TCCCDCT/VQ)-is proposed to efficiently exploit correlation In large image blocks by tak...
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In this paper, an adaptive image coding technique-two-channel conjugate classified discrete cosine transform/vector quantization (TCCCDCT/VQ)-is proposed to efficiently exploit correlation In large image blocks by taking advantage of the discrete cosine transform and vector quantization while overcoming the suboptimalities of transform coding and reducing the complexity of vector quantization. Two-channel conjugate vector quantization (TCCVQ) has recently been proposed for speech coding [1]. Compared to the ordinary VQ, TCCVQ requires less computational complexity, less storage (memory) requirement and is more robust against channel errors. This technique has also recently been extended to image coding in the spatial domain [2]. This technique is now applied to image coding In the transform domain. In the transform domain, a classified discrete cosine transform/vector quantization (CDCT/VQ) scheme is proposed and TCCCDCT/VQ is developed based on the CDCT/VQ scheme. These two techniques are applied to encode test images at about 0.51 b/pixel and 0.73b/pixel. The performances of both techniques over a noisy channel have been also tested in the transform domain. The performances of both adaptive VQ techniques are perceptually very similar for the noise-free channel case. However, when channel error is injected for the same bit rate, The TCCCDCT/VQ gives subjectively much more pleasing (less visible distortion) images than the CDCT/VQ, which is based on ordinary VQ.
In this correspondence, we address the problem of translation sensitivity of conventional wavelet transforms for two-dimensional (2-D) signals. We propose wavelet transform algorithms that achieve the following desira...
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In this correspondence, we address the problem of translation sensitivity of conventional wavelet transforms for two-dimensional (2-D) signals. We propose wavelet transform algorithms that achieve the following desirable properties simultaneously: i) translation invariance, ii) reduced edge effects, and iii) sice-limitedness. We apply this translation invariant biorthogonal wavelet transform with symmetric extensions to image coding applications with good results.
This paper deals with an image coding scheme based on overlapped projections and geometrical vector quantization. As far as still images are concerned, we present a simple coding algorithm, in which, the concept of pr...
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This paper deals with an image coding scheme based on overlapped projections and geometrical vector quantization. As far as still images are concerned, we present a simple coding algorithm, in which, the concept of projection, normally employed in tomographic applications, and geometric vector quantization are used to describe, in a compact way, circular overlapping regions in which the input image is subdivided. In this way it is possible to represent in a good way, contour and edge information without introducing significant blocking artefacts. The proposed coding techniques can also be used to represent images at progressive levels of quality. This aim can be simply obtained by increasing the number of projections used to describe each image region. Simulations carried out on several test images showed interesting results. An extension of this projection-based coding techniques is proposed for moving images too. In this case projections are used to describe in a compact way, the motion compensated luminance differences between the current image and the previous coded one. (C) 1997 Published by Elsevier Science B.V.
The authors describe and discuss the new technique, multistep adaptive flux interpolation (MAFI), and its application to image data for coding. When applied to an image, MAFI produces an output which is also in an ima...
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The authors describe and discuss the new technique, multistep adaptive flux interpolation (MAFI), and its application to image data for coding. When applied to an image, MAFI produces an output which is also in an image form, but which has a more uniform feature density and a greatly reduced size. MAFI warps the input image by removing those rows and columns which contain a majority of redundant pixels. The side information required for reconstruction is minimal, and the image can be further compressed using conventional coders, making the compression ratio even higher. Because of its warped nature, the MAFI output's statistics are also more consistent with the properties assumed by block-based discrete cosine transform (DCT) methods.
Proposed is a new algorithmic approach to segmentation-based image coding. A good compromise is achieve between segmentations by quadtree-based decomposition and by free region-growing in terms of time complexity and ...
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Proposed is a new algorithmic approach to segmentation-based image coding. A good compromise is achieve between segmentations by quadtree-based decomposition and by free region-growing in terms of time complexity and scene adaptability. Encoding is to recursively partition an image into convex n-gons, 3 less-than-or-equal-to n less-than-or-equal-to 8, until the pixels in current n-gon satisfy a uniformity criterion. The recursive partition generates a valid segmentation by aligning the polygon boundaries with image edges. This segmentation is embedded into a binary tree for compact encoding of its geometry. The compressed image is sent as a labeled pointerless binary tree, and decoding is simply polygon filling. High-compression ratios are obtained by balancing the accuracy and geometric complexity of the image segmentation;a key issue for segmentation-based image coding that was not addressed before. Due to its tree structure, the new method is also suitable for progressive image coding.
Based on importance measures and fuzzy integrals, a new assessment method for image coding quality is presented in this paper. The proposed assessment is based on two subevaluations. In the first subevaluation, errors...
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Based on importance measures and fuzzy integrals, a new assessment method for image coding quality is presented in this paper. The proposed assessment is based on two subevaluations. In the first subevaluation, errors on edges, textures, and flat regions are computed individually. The errors are then assessed using an assessment function. A global evaluation with Sugeno fuzzy integral is then obtained based on the importance measure of edge, texture, and flat region. In the second subevaluation, an importance measure is first established depending on the types of regions where errors occur, a subtle evaluation is then obtained using Sugeno fuzzy integral on all pixels of the image. A final evaluation is obtained based on the two subevaluations. Experimental results show that this new image quality assessment closely approximates human subjective tests such as mean opinion score with a high correlation coefficient of 0.963, which is a significant improvement over peak signal-to-noise ratio, picture quality scale, and weighted mean square error, three other image coding quality assessment methods, which have the correlation coefficients of 0.821, 0.875, and 0.716, respectively.
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.
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