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Compressive sampling
, 2006
"... Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired res ..."
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Cited by 1427 (15 self)
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resolution of the image, i.e. the number of pixels in the image. This paper surveys an emerging theory which goes by the name of “compressive sampling” or “compressed sensing,” and which says that this conventional wisdom is inaccurate. Perhaps surprisingly, it is possible to reconstruct images or signals
The JPEG still picture compression standard
 Communications of the ACM
, 1991
"... This paper is a revised version of an article by the same title and author which appeared in the April 1991 issue of Communications of the ACM. For the past few years, a joint ISO/CCITT committee known as JPEG (Joint Photographic Experts Group) has been working to establish the first international c ..."
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Cited by 1128 (0 self)
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compression standard for continuoustone still images, both grayscale and color. JPEG’s proposed standard aims to be generic, to support a wide variety of applications for continuoustone images. To meet the differing needs of many applications, the JPEG standard includes two basic compression methods, each
Managing Gigabytes: Compressing and Indexing Documents and Images  Errata
, 1996
"... > ! "GZip" page 64, Table 2.5, line "progp": "43,379" ! "49,379" page 68, Table 2.6: "Mbyte/sec" ! "Mbyte/min" twice in the body of the table, and in the caption "Mbyte/second" ! "Mbyte/minute" page 70, para 4, line ..."
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Cited by 985 (48 self)
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> ! "GZip" page 64, Table 2.5, line "progp": "43,379" ! "49,379" page 68, Table 2.6: "Mbyte/sec" ! "Mbyte/min" twice in the body of the table, and in the caption "Mbyte/second" ! "Mbyte/minute" page 70, para 4, line 5: "Santos" ! "Santis" page 71, line 11: "Fiala and Greene (1989)" ! "Fiala and Green (1989)" Chapter Three page 89, para starting "Using this method", line 2: "hapax legomena " ! "hapax legomenon " page 96, line 5: "a such a" ! "such a" page 98, line 6: "shows that in fact none is an answer to this query" ! "shows that only document 2 is an answer to this query" page 106, para 3, line 9: "the bitstring in Figure 3.7b" ! "the bitstring in Figure 3.7c" page 107, Figure 3.7: The coding shown in part (c) cannot be decoded ambiguously. For example, the sequence "1010 0000 0001 0000
Complete discrete 2D Gabor transforms by neural networks for image analysis and compression
, 1988
"... AbstractA threelayered neural network is described for transforming twodimensional discrete signals into generalized nonorthogonal 2D “Gabor ” representations for image analysis, segmentation, and compression. These transforms are conjoint spatiahpectral representations [lo], [15], which provide ..."
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Cited by 475 (8 self)
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AbstractA threelayered neural network is described for transforming twodimensional discrete signals into generalized nonorthogonal 2D “Gabor ” representations for image analysis, segmentation, and compression. These transforms are conjoint spatiahpectral representations [lo], [15], which
Qualitative Researching
, 1996
"... ltaic (PV) electricity production from an intermittent Since 1978, compressed air energy storage (CAES) compressed air can then be released on demand to the CAES plant’s turbogenerator set to generate premium value electricity. The first CAES plant was built in broadened in the ittency of wind g wi ..."
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Cited by 591 (0 self)
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ltaic (PV) electricity production from an intermittent Since 1978, compressed air energy storage (CAES) compressed air can then be released on demand to the CAES plant’s turbogenerator set to generate premium value electricity. The first CAES plant was built in broadened in the ittency of wind g
Multiple Description Coding: Compression Meets the Network
, 2001
"... This article focuses on the compressed representations of the pictures ..."
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Cited by 435 (9 self)
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This article focuses on the compressed representations of the pictures
EntropyBased Algorithms For Best Basis Selection
 IEEE Transactions on Information Theory
, 1992
"... pretations (position, frequency, and scale), and we have experimented with featureextraction methods that use bestbasis compression for frontend complexity reduction. The method relies heavily on the remarkable orthogonality properties of the new libraries. It is obviously a nonlinear transformat ..."
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Cited by 670 (20 self)
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transformation to represent a signal in its own best basis, but since the transformation is orthogonal once the basis is chosen, compression via the bestbasis method is not drastically affected by noise: the noise energy in the transform values cannot exceed the noise energy in the original signal. Furthermore
Progressive Meshes
"... Highly detailed geometric models are rapidly becoming commonplace in computer graphics. These models, often represented as complex triangle meshes, challenge rendering performance, transmission bandwidth, and storage capacities. This paper introduces the progressive mesh (PM) representation, a new s ..."
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Cited by 1321 (11 self)
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scheme for storing and transmitting arbitrary triangle meshes. This efficient, lossless, continuousresolution representation addresses several practical problems in graphics: smooth geomorphing of levelofdetail approximations, progressive transmission, mesh compression, and selective refinement
Exact Matrix Completion via Convex Optimization
, 2008
"... We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfe ..."
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Cited by 860 (27 self)
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by solving a simple convex optimization program. This program finds the matrix with minimum nuclear norm that fits the data. The condition above assumes that the rank is not too large. However, if one replaces the 1.2 exponent with 1.25, then the result holds for all values of the rank. Similar results hold
Boosting a Weak Learning Algorithm By Majority
, 1995
"... We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas pr ..."
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Cited by 516 (15 self)
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upper bounds known today. We show that the number of hypotheses that are combined by our algorithm is the smallest number possible. Other outcomes of our analysis are results regarding the representational power of threshold circuits, the relation between learnability and compression, and a method
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