1,096 research outputs found
A Codebook Generation Algorithm for Document Image Compression
Pattern-matching-based document-compression systems (e.g. for faxing) rely on
finding a small set of patterns that can be used to represent all of the ink in
the document. Finding an optimal set of patterns is NP-hard; previous
compression schemes have resorted to heuristics. This paper describes an
extension of the cross-entropy approach, used previously for measuring pattern
similarity, to this problem. This approach reduces the problem to a k-medians
problem, for which the paper gives a new algorithm with a provably good
performance guarantee. In comparison to previous heuristics (First Fit, with
and without generalized Lloyd's/k-means postprocessing steps), the new
algorithm generates a better codebook, resulting in an overall improvement in
compression performance of almost 17%
New approaches to texture coding in segmentation and feature-based image coding schemes
Peer ReviewedPostprint (published version
S-TREE: Self-Organizing Trees for Data Clustering and Online Vector Quantization
This paper introduces S-TREE (Self-Organizing Tree), a family of models that use unsupervised learning to construct hierarchical representations of data and online tree-structured vector quantizers. The S-TREE1 model, which features a new tree-building algorithm, can be implemented with various cost functions. An alternative implementation, S-TREE2, which uses a new double-path search procedure, is also developed. S-TREE2 implements an online procedure that approximates an optimal (unstructured) clustering solution while imposing a tree-structure constraint. The performance of the S-TREE algorithms is illustrated with data clustering and vector quantization examples, including a Gauss-Markov source benchmark and an image compression application. S-TREE performance on these tasks is compared with the standard tree-structured vector quantizer (TSVQ) and the generalized Lloyd algorithm (GLA). The image reconstruction quality with S-TREE2 approaches that of GLA while taking less than 10% of computer time. S-TREE1 and S-TREE2 also compare favorably with the standard TSVQ in both the time needed to create the codebook and the quality of image reconstruction.Office of Naval Research (N00014-95-10409, N00014-95-0G57
Implementation of Weighted Centroid Neural Network for Edge Preserving Image Compression
Image compression is a type of data compression applied to images. The objective of image compression is to reduce the cost for storage or transmission. Image compression is associated with removing redundant information of image data. Image storage is required for several purposes like document, medical images, etc. In this paper, an edge preserving image compression algorithm based on an unsupervised competitive neural network called weighted centroid neural network (WCNN), is implemented and compared to the other algorithms. The WCNN algorithm allots more representative vectors from the edges of the image than the interior of the image thus helping in better edge preservation of the reconstructed image. After experimenting with the cluster count it is evident that with the increase in the number of cluster the quality of the picture is improved, which is the expected behavior as more clusters leads to more representational vectors
WG1N5315 - Response to Call for AIC evaluation methodologies and compression technologies for medical images: LAR Codec
This document presents the LAR image codec as a response to Call for AIC evaluation methodologies and compression technologies for medical images.This document describes the IETR response to the specific call for contributions of medical imaging technologies to be considered for AIC. The philosophy behind our coder is not to outperform JPEG2000 in compression; our goal is to propose an open source, royalty free, alternative image coder with integrated services. While keeping the compression performances in the same range as JPEG2000 but with lower complexity, our coder also provides services such as scalability, cryptography, data hiding, lossy to lossless compression, region of interest, free region representation and coding
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