35 research outputs found

    DESIGN AND IMPLEMENTATION OF NON-UNIFORM QUANTIZERS FOR DISCRETE INPUT SAMPLES

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    This paper describes an algorithm for grayscale image compression based on non-uniform quantizers designed for discrete input samples. Non-uniform quantization is performed in two steps for unit variance, whereas design is done by introducing a discrete variance. The best theoretical and experimental results are obtained for those discrete values of variance which provide the operating range of quantizer located in the vicinity of maximal signal value that can appear on the entrance. The experiment is performed by applying proposed quantizers for compression of standard test grayscale images as a classic example of discrete input source. The proposed fixed non-uniform quantizers, designed for discrete input samples, provide up to 4.93 [dB] higher PSQNR compared to the fixed piecewise uniform quantizers designed for discrete input samples

    ANALYSIS OF THE IMPACT OF THE QUANTIZER RANGE CHOICE ON COMPRESSION AND QUALITY OF THE RECONSTRUCTED IMAGE

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    Abstract. In this paper an algorithm for grayscale image compression based on usage of three fixed uniform quantizers designed for discrete input samples is presented. The algorithm is based on the alternating use of these three quantizers. Number of quantization levels and quantizer range size increases from the first to the third quantizer. Experimental results show that choice of the quantizer range has an impact on system performance. While selecting a range of the first two quantizers (with a lower number of quantization levels) it is necessary to make a compromise between quality and bit rate (larger quantizer range leads to lower average bit rate but the quality of reconstructed image is also lower). It is shown that the range of the third quantizer should be set up to cover as many as possible high number of input samples making sure that the overload distortion does not become dominant

    COMPRESSION ARTIFACTS REDUCTION USING VARIATIONAL METHODS : ALGORITHMS AND EXPERIMENTAL STUDY

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    International audienceMany compression algorithms consist of quantizing the coefficients of an image in a linear basis. This introduces compression noise that often look like ringing. Recently some authors proposed variational methods to reduce those artifacts. They consists of minimizing a regularizing functional in the set of antecedents of the compressed image. In this paper we propose a fast algorithm to solve that problem. Our experiments lead us to the conclusion that these algorithms effectively reduce oscillations but also reduce contrasts locally. To handle that problem, we propose a fast contrast enhancement procedure. Experiments on a large dataset suggest that this procedure effectively improves the image quality at low bitrates

    Sparse representation based hyperspectral image compression and classification

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    Abstract This thesis presents a research work on applying sparse representation to lossy hyperspectral image compression and hyperspectral image classification. The proposed lossy hyperspectral image compression framework introduces two types of dictionaries distinguished by the terms sparse representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively. The former is learnt in the spectral domain to exploit the spectral correlations, and the latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in hyperspectral images. To alleviate the computational demand of dictionary learning, either a base dictionary trained offline or an update of the base dictionary is employed in the compression framework. The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of both SRSD and MSSD approaches. For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation. This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression. Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification.EM201

    High-performance compression of visual information - A tutorial review - Part I : Still Pictures

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    Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video telephony or multimedia applications, some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this first part, still image coding schemes have been classified into predictive, block transform, and multiresolution approaches. Predictive methods are suited to lossless and low-compression applications. Transform-based coding schemes achieve higher compression ratios for lossy compression but suffer from blocking artifacts at high-compression ratios. Multiresolution approaches are suited for lossy as well for lossless compression. At lossy high-compression ratios, the typical artifact visible in the reconstructed images is the ringing effect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second-generation coding techniques segment the image into semantically meaningful parts. Therefore, parts of these methods have been adapted to work for arbitrarily shaped regions. In order to add another functionality, such as progressive transmission of the information, specific quantization algorithms must be defined. A final step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare stateof- the-art techniques for lossy and lossless compression. The different artifacts of each technique are highlighted and discussed. Also, the possibility of progressive transmission is illustrated
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