Progressiveness and Preprocessing in Image Compression


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KOPILOVIC, Ivan, 2004. Progressiveness and Preprocessing in Image Compression

@phdthesis{Kopilovic2004Progr-6455, title={Progressiveness and Preprocessing in Image Compression}, year={2004}, author={Kopilovic, Ivan}, address={Konstanz}, school={Universität Konstanz} }

2011-03-24T16:12:49Z eng 2004 Kopilovic, Ivan Progressiveness and Preprocessing in Image Compression deposit-license application/pdf The recent developments in the multimedia communication technology made it<br />necessary to provide image compression standards with a number of<br />functionalities such as the capability of progressive transmission of the code<br />and more conformity with the human visual perception. The general purpose<br />still image compression standard JPEG2000 for example, incorporates<br />many such functionalities. This thesis contributes to the improvement of these<br />functionalities by considering two special aspects: progressive transmission<br />and preprocessing to improve the visual quality of the compressed images.<br /><br />In progressive transmission, the code for the image is sent in packets.<br />The user attempting to view the image will be shown a sequence of "previews"<br />that approximate the nal reconstruction with increasing quality, based on the<br />incoming packets. This allows the user to terminate the decoding at an<br />arbitrary point and quickly browse among a large number of images. Though<br />many practical methods use optimisation for progressive transmission, a good<br />understanding of the progressive behaviour and the optimality in this process<br />has not been given yet. We give formal definitions of the progressiveness and<br />the optimality in the progressive transmission here. Since we show that there<br />are different possibilities to define progressiveness, we are<br />going to give and prove sufficient conditions that imply the different<br />progressive properties. The linear transform-based compression will be<br />considered separately, where further sufficient conditions for progressiveness<br />are given. We use this framework to analyse the underlying<br />optimisation procedures in existing wavelet compression schemes. Our results<br />can help the design of progressive compression systems.<br /><br />In most of the image compression standards, the images are described as linear<br />combinations of given basis elements. Lossy compression is achieved by using an<br />incomplete description. There are however compression methods that use a<br />different kind of description. In fractal compression, the image is partitioned<br />into a number of regions, each of which is approximated by some appropriate<br />part of the same image. If we start with an arbitrary image and iterate these<br />approximation steps, this procedure will converge to an approximation of the<br />original image. Since the above description method is not necessarily perfect,<br />its parameters constitute a lossy compression of the image. We give an<br />optimal progressive transmission method for the fractal compression, which is<br />the first result of this kind.<br /><br />In lossy compression, various kinds of error patterns can appear on the<br />decompressed image. For example, the images compressed with JPEG suffer from<br />blocking artefacts. There is also aringing artefact observable along the edges<br />in JPEG or JPEG2000 compressed images at high compression ratios. One way of<br />alleviating these effects is to preprocess the image before compression.<br />We shall consider a previously proposed preprocessing method here, which is<br />based on edge-adaptive filtering. The filtering is achieved with non-linear<br />diffusion processes. The previous results did not give a complete understanding<br />of the underlying processes and they did not give an analysis of the different<br />parameter choices. A visual testing of the method was also<br />missing. We complete the analysis by proposing diffusion methods that are<br />appropriate for preprocessing. We consider methods for adjusting the parameters<br />for these diffusion processes. We show that they reduce the artifacts, help<br />in preserving the edges, and that they can improve the visual quality.<br />However, difficulties can arise when preprocessing images with highly irregular<br />texture. Due to visual phenomena, the visibility of artefacts is low in this<br />case. Preprocessing may yield a visible blur for such images and an inferior<br />visual quality when compared to the compression without preprocessing. Kopilovic, Ivan 2011-03-24T16:12:49Z Progressivität und Preprocessing in der Bildkompression

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