N. D. Memon and K. Sayood, "Lossless image compression: A comparative study," in Proc. SPIE (Still-Image Compression), vol. 2418, pp. 8--20, Feb. 1995.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....applications still benet from, or require, lossless compression. Such applications include scientic and medical image storage and high quality video production techniques. Lossless image compression has traditionally been based strongly on the compression of greyscale images via predictive coding[1]. Colour images and video have generally been treated as a set of unrelated greyscale planes and a sequence of unrelated frames respectively. However, some techniques have been reported in the literature that use the extra redundancy in colour images and video to obtain extra compression [2, 3, 4, ....

N. Memon and K. Sayood. Lossless image compression: A comparative study. Proc. SPIE Still-Image Compression, 2418:8--20, March 1995.

....are made to detect transmission errors and data found to be in error is discarded so as not to pollute the decompressed output. This is achieved while still maintaining a level of compression that is competitive with lossless image compression standards. 1 Introduction Lossless image compression [1] is a useful technique for the storage and transmission of medical, scientific and pre production image material. Although offering only moderate compression (rarely much better than 2:1) lossless techniques guarantee reversible decoding in the absence of channel noise. However, almost all ....

N. Memon and K. Sayood. Lossless image compression: A comparative study. Proc. SPIE Still-Image Compression, 2418:8--20, March 1995.

....P(x ## #S ## ) S C, 4.3) is maximized, where S is the context used to condition the probability assigned. Due to the fact that images are digitized from analogue signals, strong correlation exists among neighbouring pixels. In practice, therefore, various prediction techniques [22,27,35,43,44,71] are developed to assist with the above inferences. The idea is to de correlate the pixel data in such a way that the residue of the prediction becomes an independent random data set. Hence, better inferences can be made to maximize the probabilities allocated to all error entries, and the entropy ....

....a way that the residue of the prediction becomes an independent random data set. Hence, better inferences can be made to maximize the probabilities allocated to all error entries, and the entropy of the error data set can also be minimized correspondingly. As a matter of fact, comparative studies [43,71] reveal that the main redundancy reduction is achieved through the inferences of the maximum probability assigned to each pixel encoded. The context based algorithm [71] for instance, gives the best performance according to the results reported in the literature. Normally, the criterion for ....

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N.D. Memon, K. Sayood, Lossless image compression: A comparative study, in: Proc. SPIE Still Image Compression, Vol. 2418, 1995, pp. 8}27.

....These methods are usually used for lossless image compression. Lossless image coding plays an important role in situations such as medical imaging or satellite photography, where no information loss is allowed during compression. A recent survey of lossless compression techniques is presented in [10]. Part of the review presented in [11] is devoted to lossless image compression techniques with special focus on radiological image compression. For all types of images, direct coding using an entropy coder does not achieve any considerable degree of compression. As an example, consider the ....

N. D. Memon and K. Sayood, "Lossless image compression: A comparative study," in Proc. SPIE, vol. 2418, 1995, pp. 8--20.

....permutation of the elements of a rasterscanned vector of image samples as a scan order: an alternative (i.e. other than the given raster scan) way of visiting the given collection of data points. The idea of using better scan orders to improve on prediction accuracy is quite old; the survey paper [9] provides a good recent summary of prior work in this direction. Work on alternative scan orders has focused on local scan orders and prediction error estimates as a measure of goodness. Selection of the best scan order is accomplished either by exhaustive search or by minimum weight spanning tree ....

N. D. Memon and K. Sayood, "Lossless image compression---A comparative study," Proc. SPIE 2418 Still Image Compression, pp. 8--20, 1995.

....B in the example) The Hilbert curve [Fig. 4(b) indeed generates quite compact data regions. Additionally, it supports the prediction step quite well because of the spatial adjacency of consecutive pixels in the sequence. Note that compression along Hilbert curves has been proposed earlier [28] [30], but with a different objective. In this paper, we focus on the interplay between compression and spatial access, since optimizing either of both alone does not support spatial queries in compressed images. Prediction is usually based on neighboring pixels in space. However, the pixels used as ....

N. D. Memon and K. Sayood, "Lossless image compression: A comparative study," in Proc. SPIE Conf. Electronic Imaging, 1995, pp. 8--20.

....Tables 1 and 2 list the lossless bit rates of the proposed lossless image coder and some other related coders. The results of Table 1 are given in entropy, whereas the results of Table 2 are actual bit rates. The compression results of other algorithms in Tables 1 were reported in a survey paper [8]. The bit rates of PPM and VQ prediction method are quoted from [3] and [13] respectively. The JPEG bit rates were obtained by the best of the eight DPCM predictors suggested by JPEG with static Huffman coding. The reported bit rates of the new method were obtained without any optimization with ....

N. Memon and K. Sayood, "Lossless image compression: a comparative study", pre-print, 1995.

....Quite a few techniques have been developed for watermarking images [4] video [5] and audio [6] Unlike this work, these techniques involve watermarking the media, and not the media codec. There have been numerous papers on linear and non linear prediction for lossless image compression [7,8] from different application domains. Most of these papers describe the different x,y coordinate pixels to use to do linear prediction more efficiently for the images of different types, e.g. medical images, synthetic aperture radar images, infrared images, and others. All these papers present a ....

N. D. Memon, K. Sayood, and S. S. Magliveras. "Lossless Image Compression -- a comparative study." Still Image Compression, pages 8-20. SPIE Proceedings Volume 2418, 1995.

....first step to compression is decorrelation. There are many decorrelation schemes such as wavelet, hierarchical interpolation, and prediction etc. In the context of lossless nearlossless image compression, experiments show that there is no obvious difference between the performance of these methods [1][2] but their computational complexities are very different. Actually, seven out of the nine proposals for JPEG LS adopted the prediction method with simplicity in mind [3] Within the framework of prediction based compression, there are three modules; including prediction model, statistical ....

....texture orientation is accurate and robust. B. Prediction Performance of BAROLTO We compare the prediction performance of BAROLTO with that of other famous predictors in Tab. 1. The values of zero order entropy of the prediction error on JPEG test set are listed. The data in column 2 to 6 are from [1][2] BJPEG is the best one of the seven DPCM predictors of JPEG. MAP, OLP, HINT and SCAN are all famous prediction methods, in which MAP is actually adopted by LOCO I. We can see that our method consistently outperforms all the other predictors by a large margin. The predictor of CALIC, GAP, is ....

N. Memon, K. Sayood. "Lossless Image Compression: A Comparative Study", Proc. SPIE, Vol. 2814, pp8-20, 1995.

....that resembles one of the associated basis functions as closely as possible, in a least squares (LS) sense. The idea of using permutations for (stand alone) source coding has been investigated in the mid 60 s to early 80 s by Berger et al. 4] 5] 6] This and other related work [7] 8] [9], 10] is reviewed in detail in the sequel (Section III) The idea of using a globally optimal permutation April 22, 1998 DRAFT 3 for effective transform domain energy compaction of broadband signals is a contribution of this paper. A. Organization This paper is structured as follows. The main ....

....of the elements of a raster scanned vector of image samples as a scan order: an alternative (i.e. other than the given raster scan) way of visiting the given collection of data points. The idea of using better scan orders to improve on prediction accuracy is quite old: the survey paper [9] provides a good recent summary of prior work in this direction. The Hilbert space filling curve is a well known example of an alternative scan order. Work on alternative scan orders (cf. 9] and the references therein) has been focused on local scan orders, meaning scan orders which only allow ....

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N.D. Memon and K. Sayood, "Lossless Image Compression - A Comparative Study", in Still Image Compression, Proc. SPIE 2418, 1995, pp. 8--20.

....Martucci reported the best results with the following three predictors, in which case it is easy to see that MAP turns out to be the MED predictor. 1. N 2. W 3. N W NW. In an extensive evaluation, the MED predictor was observed to give superior performance over most linear predictors [16]. 3.2. The GAP predictor The CALIC proposal [17] included a gradient adjusted predictor (GAP) which adapts the prediction according to local gradients and hence gives a more robust performance compared to standard linear predictors. GAP weights the neighbouring pixels of P[i, j ] according to ....

Memon, N. D. and Sayood, K. (1995) Lossless image compression---a comparative study. In Still Image Compression, SPIE Proc., Vol. 2418, pp. 8--20.

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N. D. Memon and K. Sayood, "Lossless image compression: A comparative study," in Proc. SPIE (Still-Image Compression), vol. 2418, pp. 8--20, Feb. 1995.

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N. D. Memon and K. Sayood, "Lossless Image Compression: A Comparative Study," Proceedings of the SPIE, vol. 2418, pp. 8--20, 1995.

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N.D. Memon, K. Saywood, "Lossless image compression: a comparative study", Proc. SPIE, vol. 24#8, #995, pp. 820.

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N. D. Memon and K. Sayood, `Lossless image compression: A comparative study,' Proceedings SPIE, vol. 2418, pp. 8-20, February 1995.

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