@article {15613,
	title = {An efficient k-means clustering algorithm: analysis and implementation},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {24},
	year = {2002},
	month = {2002/07//},
	pages = {881 - 892},
	abstract = {In k-means clustering, we are given a set of n data points in d-dimensional space R<sup>d</sup> and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd{\textquoteright}s (1982) algorithm. We present a simple and efficient implementation of Lloyd{\textquoteright}s k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm{\textquoteright}s running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation},
	keywords = {algorithm;color, algorithm;image, algorithm;kd-tree;mean, analysis;filtering, clustering, clustering;, compression;data, distance;covariance, Lloyd, matrices;filtering, quantization;data, segmentation;k-means, squared, structure;data-sensitive, theory;pattern},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2002.1017616},
	author = {Kanungo,T. and Mount, Dave and Netanyahu,N. S and Piatko,C. D and Silverman,R. and Wu,A. Y}
}
@article {13633,
	title = {Residual coding in document image compression},
	journal = {Image Processing, IEEE Transactions on},
	volume = {9},
	year = {2000},
	month = {2000/06//},
	pages = {961 - 969},
	abstract = {Symbolic document image compression relies on the detection of similar patterns in a document image and construction of a prototype library. Compression is achieved by referencing multiple pattern instances ( ldquo;components rdquo;) through a single representative prototype. To provide a lossless compression, however, the residual difference between each component and its assigned prototype must be coded. Since the size of the residual can significantly affect the compression ratio, efficient coding is essential. We describe a set of residual coding models for use with symbolic document image compression that exhibit desirable characteristics for compression and rate-distortion and facilitate compressed-domain processing. The first model orders the residual pixels by their distance to the prototype edge. Grouping pixels based on this distance value allows for a more compact coding and lower entropy. This distance model is then extended to a model that defines the structure of the residue and uses it as a basis for continuous and packet reconstruction which provides desired functionality for use in lossy compression and progressive transmission},
	keywords = {coding, coding;compressed-domain, coding;entropy;lossless, coding;image, communication;, compact, compression;data, compression;document, compression;multiple, construction;rate-distortion;representative, detection;symbolic, difference;residual, distortion, document, edge;prototype, image, library, model;efficient, models;residual, pattern, patterns, pixels;similar, processing;compression, processing;entropy;image, prototype;residual, ratio;continuous, reconstruction;distance, reconstruction;progressive, reconstruction;rate, referencing;packet, theory;visual, transmission;prototype},
	isbn = {1057-7149},
	doi = {10.1109/83.846239},
	author = {Kia,O. E and David Doermann}
}
@conference {18289,
	title = {Video transcoding by reducing spatial resolution},
	booktitle = {Image Processing, 2000. Proceedings. 2000 International Conference on},
	volume = {1},
	year = {2000},
	month = {2000///},
	pages = {972 -975 vol.1 - 972 -975 vol.1},
	abstract = {In network delivery of digital video, if the bandwidth required for a video is not available, the video has to be recoded at a reduced bit rate. It is highly desirable that the transcoding is carried out in real time while maintaining reasonable image quality. In this paper, we propose a fast approach to derive from an MPEG stream a new MPEG stream with half the spatial resolution. For the downsized video, we first generate from the original compressed video an improved estimate of the motion vectors. We then propose a compressed domain approach with data hiding to produce DCT residues by an open-loop method. The computational complexity is significantly lower than a number of previous approaches. Simulation suggests that our approach produces reasonable image quality},
	keywords = {approach;computational, coding;, coding;video, complexity;data, compression;data, cosine, DCT, delivery;open-loop, domain, encapsulation;discrete, estimation;network, estimation;transform, hiding;digital, method;spatial, quality;motion, reduction;video, residues;MPEG, resolution, resolution;motion, stream;compressed, transcoding;computational, transforms;image, vectors, video;downsized, video;image},
	doi = {10.1109/ICIP.2000.901123},
	author = {Yin,Peng and M. Wu and Liu,Bede}
}
@conference {13612,
	title = {OCR-based rate-distortion analysis of residual coding},
	booktitle = {Image Processing, 1997. Proceedings., International Conference on},
	volume = {3},
	year = {1997},
	month = {1997/10//},
	pages = {690 -693 vol.3 - 690 -693 vol.3},
	abstract = {Symbolic compression of document images provides access to symbols found in document images and exploits the redundancy found within them. Document images are highly structured and contain large numbers of repetitive symbols. We have shown that while symbolically compressing a document image we are able to perform compressed-domain processing. Symbolic compression forms representative prototypes for symbols and encode the image by the location of these prototypes and a residual (the difference between symbol and prototype). We analyze the rate-distortion tradeoff by varying the amount of residual used in compression for both distance- and row-order coding. A measure of distortion is based on the performance of an OCR system on the resulting image. The University of Washington document database images, ground truth, and OCR evaluation software are used for experiments},
	keywords = {analysis;redundancy;representative, character, coding;distortion, coding;image, coding;lossy, coding;row-order, coding;symbolic, compression;data, compression;document, compression;lossy, database, distortion, Evaluation, image, images;document, images;experiments;ground, measure;document, OCR, of, performance;University, processing;distance-order, processing;image, prototypes;residual, recognition;rate, representation;optical, representation;progressive, software;OCR, system, theory;, transmission;rate-distortion, truth;image, Washington;compressed-domain},
	doi = {10.1109/ICIP.1997.632215},
	author = {Kia,O. E and David Doermann}
}
