Interband coding techniques are needed for effective compression of hyperspectral images,since high interband correlation cannot be exploited by intraband *** this letter,an interband version of GAP（gradient adjusted...
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ISBN:
(纸本)9781467349970
Interband coding techniques are needed for effective compression of hyperspectral images,since high interband correlation cannot be exploited by intraband *** this letter,an interband version of GAP（gradient adjusted prediction） is proposed by combining a linear prediction with a gradient adjusted *** corresponding prediction function is chose by comparing the difference between the estimate of horizontal gradients and that of vertical gradients with a given *** prediction,the difference is entropy-coded using an adaptive entropy *** results on Airborne Visible/Infrared Imaging Spectrometer（AVIRIS） data show the proposed algorithm can exploit both interband and intraband statistical correlations,and achieve better compression performance compared with those existing classical ***,low encoder complexity makes it suitable for on-board compression of hyperspectral images.
Modern seismic exploration produces vast amount of data that may exceed 100-Tbytes. Besides that, as more data are processed and integrated on workstations, more data transfer among the workstations through the local ...
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ISBN:
(纸本)9780780395091
Modern seismic exploration produces vast amount of data that may exceed 100-Tbytes. Besides that, as more data are processed and integrated on workstations, more data transfer among the workstations through the local area networks is required. High compression algorithms are then desirable to make seismic data procession more efficient in terms of storage and transmission bandwidth. Most of current algorithms used for seismic data compression are based on wavelet or LCT (local cosine transform), which can only achieve modest compression rations and may result in visible degradation in high rates compression. In this paper, an adaptive seismic data compression method is presented based on wavelet packets transform, which can achieve higher compression rates and have no visible artifacts in reconstructed data.
In this work, an efficient and robust learning-based JPEG2000 architecture is proposed. It uses machine learning techniques for predicting and encoding the decision bit in the embedded block coding with optimized trun...
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ISBN:
(纸本)9781728180687
In this work, an efficient and robust learning-based JPEG2000 architecture is proposed. It uses machine learning techniques for predicting and encoding the decision bit in the embedded block coding with optimized truncation (EBCOT) process. First, we apply non-locally weighted ridge regression to predict the quantized wavelet coefficients in the LL subband. Then, during the EBCOT process, we perform inter/intra subband prediction and inter/intra bit plane symbol prediction to estimate the activity of the decision bit using the deep learning architecture. Then, the binary prediction result is treated as an additional context and the decision bit is eventually coded using an advanced context-based adaptive binary arithmetic coder. Simulations show that the proposed framework provides the same visual quality as conventional codecs with as much as 30% bitrate savings.
Zerotree,bit plane and arithmeticcoding are widely used in image compression algorithms based on wavelet transform. By exploiting the correlation among wavelet coefficients and combining the zerotree,bit plane and ar...
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Zerotree,bit plane and arithmeticcoding are widely used in image compression algorithms based on wavelet transform. By exploiting the correlation among wavelet coefficients and combining the zerotree,bit plane and arithmeticcoding, this paper presents a new image compression algorithm,named CZ-BPP (context-based zeortree and bit plane prediction coding).Experimental results illustrate that it performs better than EZW and SPIHT.
Cílem této práce je popsat kontextové kompresní metody a jejich aplikaci na multimediální data. Je zde popsán princip aritmetického kódování a metody predict...
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Cílem této práce je popsat kontextové kompresní metody a jejich aplikaci na multimediální data. Je zde popsán princip aritmetického kódování a metody prediction by partial matching včetně tvorby pravděpodobnostního modelu. Také jsou popsány multimediální data a základní principy jejich komprese. V další části jsou prezentovány kompresní metody, které jsem implementoval v práci a jejich výsledky.
Cílem této diplomové práce bylo navrhnout, vytvořit a otestovat metodu pro bezeztrátovou kompresi obrazu. Teoretická část zahrnuje popis vybraných exitujících metod ja...
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Cílem této diplomové práce bylo navrhnout, vytvořit a otestovat metodu pro bezeztrátovou kompresi obrazu. Teoretická část zahrnuje popis vybraných exitujících metod jako jsou RLE, MTF, adaptivní aritmetické kódování, barevné modely použité v metodách LOCO-I a JPEG 2000, prediktory MED, GAP a laplaceova pyramida. Závěr práce obsahuje srovnání různých kombinací vybraných přístupů a celkové porovnání s efektivitou metod PNG a JPEG-LS.
We propose a new adaptive block-wise lossless image compression algorithm, which is based on the so-called alphabet reduction scheme combined with an adaptive arithmetic coding (AC). This new encoding algorithm is par...
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We propose a new adaptive block-wise lossless image compression algorithm, which is based on the so-called alphabet reduction scheme combined with an adaptive arithmetic coding (AC). This new encoding algorithm is particularly efficient for lossless compression of images with sparse and locally sparse histograms. AC is a very efficient technique for lossless data compression and produces a rate that is close to the entropy;however, a compression performance loss occurs when encoding images or blocks with a limited number of active symbols by comparison with the number of symbols in the nominal alphabet, which consists in the amplification of the zero frequency problem. Generally, most methods add one to the frequency count of each symbol from the nominal alphabet, which leads to a statistical model distortion, and therefore reduces the efficiency of the AC. The aim of this work is to overcome this drawback by assigning to each image block the smallest possible set including all the existing symbols called active symbols. This is an alternative of using the nominal alphabet when applying the conventional arithmetic encoders. We show experimentally that the proposed method outperforms several lossless image compression encoders and standards including the conventional arithmetic encoders, JPEG2000, and JPEG-LS. (C) 2015 SPIE and IS&T
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