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William A. Pearlman
Researcher at Rensselaer Polytechnic Institute
Publications - 202
Citations - 13136
William A. Pearlman is an academic researcher from Rensselaer Polytechnic Institute. The author has contributed to research in topics: Data compression & Set partitioning in hierarchical trees. The author has an hindex of 36, co-authored 202 publications receiving 12924 citations. Previous affiliations of William A. Pearlman include Texas A&M University & University of Wisconsin-Madison.
Papers
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Journal ArticleDOI
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
Amir Said,William A. Pearlman +1 more
TL;DR: The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods.
Journal ArticleDOI
An image multiresolution representation for lossless and lossy compression
Amir Said,William A. Pearlman +1 more
TL;DR: A new image multiresolution transform that is suited for both lossless (reversible) and lossy compression, and entropy obtained with the new transform is smaller than that obtained with predictive coding of similar complexity.
Journal ArticleDOI
Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees (3-D SPIHT)
TL;DR: A low bit-rate embedded video coding scheme that utilizes a 3-D extension of the set partitioning in hierarchical trees (SPIHT) algorithm which has proved so successful in still image coding, which allows multiresolutional scalability in encoding and decoding in both time and space from one bit stream.
Journal ArticleDOI
Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm
TL;DR: A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed and is significantly more efficient in compression and in computation than previously proposed ECG compression schemes.
Proceedings ArticleDOI
Steganalysis of additive-noise modelable information hiding
TL;DR: In this article, it is shown that these embedding methods are equivalent to a lowpass filtering of histograms that is quantified by a decrease in the HCF center of mass (COM), which is exploited in known scheme detection to classify unaltered and spread spectrum images using a bivariate classifier.