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Hau Lai Ho, CUHK (Hong Kong)
Wai Kuen Cham, CUHK (Hong Kong)
The usual attractor coding technique is to partition a given image into a number of non-overlapping range blocks. Each block of the partition is expressed as the contractive transformation of another part of the image. However, the non-overlapping partitioning induces blocking artifacts which is highly disturbing to human visual system. In this work, a novel coding scheme using iterated function systems with lapped range blocks (LPIFS) is proposed. Each range block laps with its adjacent blocks through a weighing window which has diminishing magnitudes towards its borders. In order to avoid blurring of image details, the transformation parameters are computed such that the aliasing error is compensated. The contractivity of the proposed transformation is also proved. Experiments show that a very significant improvement of the visual quality of the decoded images with nearly no loss in image details.
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Farshid Golchin, Griffith University (Australia)
Kuldip K. Paliwal, Griffith University (Australia)
In this a paper a quadtree based method is proposed for classifying blocks of samples in image subbands. Classification of blocks of subband samples according to their energy and variable bit allocation within the subsequent classes has demonstrated considerable gains in coding efficiency. The gains due to classification increase as smaller blocks are used; however, so do the overheads for transmitting the classification information. The quadtree based method proposed in this paper allows for more efficient classification by using variable-sized blocks in order to maximize the classification gain, while maintaining a limit on the classification overheads. Using an efficient quantization scheme such as ACTCQ (Arithmetic and Trellis Coded Quantization), we have been able to demonstrate competitive coding results at low bit-rates.
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Murari Srinivasan, University of Maryland (U.S.A.)
Rama Chellappa, University of Maryland (U.S.A.)
This paper proposes a new adaptive source-channel coding scheme in the context of subband coding. We first express the total mean-squared distortion suffered by the source in terms of source and channel distortions of the subbands. We then minimize this total distortion by an appropriate choice of source and channel coding rates for the subbands. This corresponds to casting the conventional bit allocation problem in a joint source-channel coding context. The choice of rates depends on the state of the physical channel, modeled by a binary symmetric channel (BSC). We then use a finite state Markov model for a fading channel to generalize results obtained for the BSC. This results in a joint source-channel coding scheme that is optimized to the current state of a fading channel.
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Giovanni Motta, Brandeis University (U.S.A.)
Bruno Carpentieri, University of Salerno (Italy)
We present a new Trellis Coded Vector Residual Quantizer (TCVRQ) that combines trellis coding and vector residual quantization. We propose new methods for computing quantization levels and experimentally analyze the performances of our TCVRQ in the case of still image coding. Experimental comparisons show that our quantizer performs better than the standard Tree and Exhaustive Search Quantizers based on the Generalized Lloyd Algorithm (GLA).
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Paul D. Wakefield, University of Bath (U.K.)
David M. Bethel, University of Bath (U.K.)
Donald M. Monro, University of Bath (U.K.)
The performance of any block based image coder can be improved by applying fractal terms to selected blocks. Two novel methods are used to achieve this. Firstly the coder determines whether a local fractal term will improve each image block by examining its rate/distortion contribution, so that only beneficial fractal terms are used. Secondly, the decoder deduces the offset parameters for the local fractal transform from the basis functions alone, by inferring the dominant edge position, so that no offset information is required. To illustrate the method, we use a quadtree decomposed image with a truncated DCT basis. Using a standard test image, the proportion of the picture area enhanced by fractals decreases from 16.1% at 0.6 bpp to 8.1% at a high compression ratio of 80:1 (0.1bpp). The fractal terms contribute less than 5% of the compressed code in all cases. The PSNR is improved slightly, and edge detail is visually enhanced.
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Jeroen Van Overloop, University of Gent (Belgium)
Wilfried Philips, University of Gent (Belgium)
Dimitri Torfs, University of Gent (Belgium)
Ignace Lemahieu, University of Gent (Belgium)
This paper adresses the efficient high-compression coding of palettized color images. The most common methods for the lossy compression of color images rely on independent block oriented transform coding of the three or four color components. These techniques do not make use of the high redundancy of the color components and introduce some very undesirable errors at high compression, in particular block distortion. We present an efficient and original technique to code color images with a small number of colors. This is an important class of images in multimedia applications. The technique codes the according luminance image using an existing segmented image coding method for monochrome images. The color information is independently represented in a bit map. The method does not rely on the commonly used color separation and shows a far better subjective image quality than JPEG at high compression.
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Koen Denecker, University of Gent (Belgium)
Peter De Neve, University of Gent (Belgium)
The huge sizes of screened colour-separated photographic images makes lossless compression very beneficial for both storage and transmission. Because of the special structure induced by the half-tone dots, the compression results obtained on the CCITT test images might not apply to high-resolution screened images and the default parameters of existing compression algorithms may not be optimal. In this paper we compare the performance of different classes of lossless coders: general-purpose one-dimensional coders, non-adaptive two-dimensional black-and-white coders and adaptive two-dimensional coders. Firstly, experiments on a set of test images screened under different conditions showed that MGBILEVEL and JBIG perform best with respect to compression efficiency; the difference with the other coders is significant. Secondly, we investigated the influence of the screening method (stochastic or classical screening) and screening resolution on the compression ratio for these techniques.
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Matthew C. Forman, De Montfort University. (U.K.)
Amar Aggoun, De Montfort University. (U.K.)
Malcolm McCormick, De Montfort University. (U.K.)
A unique integral imaging system is employed as part of a three dimensional television system, allowing display of full colour 3D images with continuous parallax within a wide viewing zone. A significant quantity of data is required to represent captured integral 3D images with adequate resolution. In this paper a lossy compression scheme is described, based on the use of a three dimensional discrete cosine transform (3D-DCT), which makes possible efficient storage and transmission of such images while maintaining all information necessary to produce a high quality 3D display. The results of simulations performed using the 3D-DCT algorithm are presented and it is shown that rate-distortion performance is vastly improved compared with that achieved using an established lossy compression scheme, baseline JPEG, with captured integral 3D image data.
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Leszek Cieplinski, University of Surrey (U.K.)
Miroslaw Bober, University of Surrey (U.K.)
We present a new approach to multiresolution vector quantisation. Its main advantage is exploitation of long-range correlations in the image by keeping vector size constant, independent of the image scale. We also developed a variable block-rate version of the algorithm, which allows better utilisation of the available bit budget by refining only those areas of the image which are not efficiently approximated by lower resolutions of the pyramid.
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Lin Cheng Wan, SUNY (U.S.A.)
Syed A. Rizivi, City University of New York (U.S.A.)
Nasser M. Nasrabadi, ARTR (U.S.A.)
This paper presents a predictive residual vector quantization (PRVQ) scheme using a modular neural network vector predictor. The proposed PRVQ scheme takes the advantage of the high prediction gain and the improved edge fidelity of a modular neural network vector predictor in order to implement a high performance vector quantization (VQ) scheme with low search complexity and a high perceptual quality. Simulation results show that the proposed PRVQ with modular vector predictor outperforms the equivalent PRVQ with general vector predictor (operating at the same bit rate) by more than $1 dB$. Furthermore, the perceptual quality of the reconstructed image is also improved.
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Robert Buccigrossi, University of Pennsylvania (U.S.A.)
Eero Simoncelli, NYU (U.S.A.)
We present a wavelet image coder based on an explicit model of the conditional statistical relationships between coefficients in different subbands. In particular, we construct a parameterized model for the conditional probability of a coefficient given coefficients at a coarser scale. Subband coefficients are encoded one bitplane at a time using a non-adaptive arithmetic encoder. The overall ordering of bitplanes is determined by the ratio of their encoded variance to compressed size. We show rate-distortion comparisons of the coder to first and second-order theoretical entropy bounds and the EZW coder. The coder is inherently embedded, and should prove useful in applications requiring progressive transmission.
ic972957.pdf ic972957.pdf TOPMichelle Effros, California Institute of Technology, Pasadena, CA (U.S.A.)
We consider the problem of lossy source coding for transmission across an unknown or time-varying noisy channel. The objective is to design an optimal compression system for applications where the unknown channel characteristics are independently estimated at the channel encoder and decoder. Channel estimation reliability is allowed to vary from perfect to no channel identification. In each case, the goal in system design and operation is to achieve the best possible expected performance. We describe an optimal design technique and an algorithm for achieving optimal expected performance for the entire array of channel estimation accuracies. The resulting system achieves up to 9 dB improvement over the performance on a system designed assuming zero probability of error when used to encode a collection of medical brain scans for transmission across a finite state channel containing two equally probable binary symmetric channels with crossover probabilities .05 and .001.
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Doina Petrescu, Tampere University of Technology (Finland)
Ioan Tabus, Tampere University of Technology (Finland)
Moncef Gabbouj, Tampere University of Technology (Finland)
This paper proposes the use of MAE--optimal Boolean and stack filters for sequential prediction in lossless grey-level image coding. FIR--Boolean hybrid filters are introduced as variations of Boolean filter structure and shown to be very effective for the prediction task. Different instances of optimal filtering are considered for realizing the prediction stage. First, the use of global--optimal predictors is analyzed, when the global MAE--optimal filter is used as a predictor. Then more refined structures, block--optimal and adaptive--size--block--optimal are considered, where predictors are adapted to local characteristics. These structures prove most suitable when small prediction masks are used. Extensive simulations are carried out for analyzing and comparing the performance of the newly introduced predictors and various other sequential predictors.
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Khanh Nguyen-Phi, TU-WIEN (Austria)
Hans Weinrichter, TU-WIEN (Austria)
Image coding using Discrete Wavelet Transform can attain very good results compared to traditional methods. In fact, most of the best known results for image coding have been related to DWT. A simple scheme to code the DWT coefficients is the Embedded zero-tree wavelet algorithm by Shapiro. Embedded zero-tree wavelet can exploit both the similarity among subbands and the sparseness of the subbands. In this paper, we propose a new simple image coder based on DWT. The DWT coefficients are coded in bitplanes. Each bi-level pixel is coded in a context formed by its parental and surrounding pixels. In this way, both the similarity and the sparseness among subbands are exploited. We show the experimental results, both in terms of distortion measurement and visual comparison, and compare them to well-known methods.
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