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Near lossless image compression by local packing of histogram
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008In this paper a low complexity algorithm is proposed for near lossless compression of images. The reconstructed near lossless image can differ from the original one within a pixelwise error tolerance. This property is used to convert the histogram of the original image, by the proposed algorithm, to a new histogram which is proved to have minimum ...
Ebrahim Nasr-Esfahani +3 more
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A method for progressive near-lossless image compression
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2004In this paper, we propose an algorithm, which allows for embedded coding in L/sub /spl infin// sense, i.e., progressive near-lossless as well as lossless image compression. The method is based on a lossy plus near-lossless refinement layered compression scheme. As a basis for the lossy layer we use the JPEG2000 standard.
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An Efficient Near-lossless Compression Algorithm for Multichannel EEG signals
2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2021In many biomedical measurement procedures, it is important to record a huge amount of data, to monitor the state of health of a subject. In such a context, electroencephalograph (EEG) data are one of the most demanding in terms of size and signal behavior. In this paper, we propose a near-lossless compression algorithm for EEG signals able to achieve a
Campobello G. +8 more
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Near-lossless compression of digital terrain elevation data
SPIE Proceedings, 2004In critical applications of image compression that are sensitive to information loss, lossless compression techniques are usually employed. The compression ratios obtained from lossless techniques are low. Hence we need different schemes that give quantitative guarantees about the type and amount of distortion incurred, viz.
Ramesh V. Panchagnula +1 more
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Optimal subtractive dither for near-lossless compression
Proceedings DCC 2000. Data Compression Conference, 2002Subtractive dither is a technique which may be used to reduce the occurrence of compression artifacts from near-lossless compression. Standard subtractive dither incurs a cost, however, in the form of an increase in rate and distortion, and by giving the reconstructed signal an overall grainy appearance.
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Extending TMW for near lossless compression of greyscale images
Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225), 2002We present a general purpose lossless greyscale image compression method, TMW, that is based on the use of linear predictors and implicit segmentation. We then proceed to extend the presented methods to cover near lossless image compression. In order to achieve competitive compression, the compression process is split into an analysis step and a coding
Bernd Meyer 0001, Peter E. Tischer
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Fast-adaptive near-lossless image compression
Journal of Electronic Imaging, 2016The purpose of image compression is to store or transmit image data efficiently. However, most compression methods emphasize the compression ratio rather than the throughput. We propose an encoding process and rules, and consequently a fast-adaptive near-lossless image compression method (FAIC) with good compression ratio. FAIC is a single-pass method,
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Distributed near lossless compression algorithm for hyperspectral images
Computers & Electrical Engineering, 2014Abstract This paper presents a new near lossless compression algorithm for hyperspectral images based on distributed source coding. The algorithm is performed on blocks that have the same location and size in each band. Because the importance varies from block to block along the spectral orientation, an adaptive rate allocation algorithm that weights
Yongjian Nian, Mi He, Jianwei Wan
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Application of Discrete Atomic Compression to Near Lossless Image Compression
2020 IEEE Ukrainian Microwave Week (UkrMW), 2020In this paper, we consider discrete atomic compression (DAC), which is lossy image compression algorithm based on atomic functions. One of its key features is a possibility to provide a desired loss of quality measured by maximum absolute deviation (MAD).
Viktor Makarichev +2 more
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Near-Lossless 3D-image Compression Using Hypergraphs
Data Compression Conference (DCC'06), 2006Summary form only given. We extend the hyper graph-based image representation to SD-images. This extended representation conducts to a generalisation of the HLC lossless compression algorithm for near-lossless 3D-image compression: HNLC.
Luc Gillibert, Alain Bretto
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