Author
William A. Pearlman
Other affiliations: Texas A&M University, University of Wisconsin-Madison
Bio: 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 published on a yearly basis
Papers
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23 Mar 1992
TL;DR: A new algorithm for multirate vector quantization is used for coding image pyramids, called alphabet and entropy-constrained pairwise-nearest-neighbor (AECPNN).
Abstract: A new algorithm for multirate vector quantization is used for coding image pyramids. The algorithm, called alphabet and entropy-constrained pairwise-nearest-neighbor (AECPNN), designs codebooks choosing subcodebooks from a large generic codebook. The algorithm is the natural extension of the ECPNN design technique with constrained alphabet. Results of coded image pyramids obtained using the present algorithm are comparable to results for the ECPNN design technique. >
3 citations
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10 Sep 2000TL;DR: A semi-automatic algorithm to extract the semantic video object from image sequences is proposed, which gets the initial video object in the first frame and other frames of a sequence.
Abstract: A semi-automatic algorithm to extract the semantic video object from image sequences is proposed. Different schemes are used to get the initial video object in the first frame and other frames of a sequence. In the first frame, two polygons are input by the user to specify the area in which the object boundary is located. Then the video object is extracted automatically based on only the first frame. In the following frames, the image frame is segmented into intensity homogeneous regions. The moving regions are detected by a morphological filter, non-moving regions are selected by the object model obtained from the previous frame. These regions form the initial video object. In each frame, after the initial object is available, the edges which belong to the video object of interest are selected by a local object contour model. Finally, an active contour model (snake) is applied to extract the final object contour.
3 citations
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01 Apr 1981TL;DR: A non-adaptive scheme for encoding an image sequence is described that yields better results than any previous comparable interframe image coding method and uses a full image DFT and an optimal quantization algorithm.
Abstract: A non-adaptive scheme for encoding an image sequence is described that yields better results than any previous comparable interframe image coding method. The scheme uses a hybrid of two-dimensional transform coding for the spatial dimension and DPCM for the time dimension similar to some previous works [e.g., 1]. Unlike the previous efforts, which use discrete cosine transforms (DCT's) of image subblocks and suboptimal quantization, we utilize a full image DFT and an optimal quantization algorithm. The results with the standard sixteen frame sequence of Walter Cronkite are visually superior to previous transform coding efforts, especially at rates lower than 1 bit/pixel/ frame. The signal-to-noise ratios are higher than those of nonadaptive hybrid DCT/DPCM in [1] by 2.6 to 10.5 dB depending on rate.
3 citations
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22 Oct 1993TL;DR: Two different approaches to image sequence coding are presented which exploit the spatial frequency statistics as well as the spatial and temporal correlation present in the video signal to achieve coding performance comparable to full-search vector quantization.
Abstract: The paper presents two different approaches to image sequence coding which exploit the spatial frequency statistics as well as the spatial and temporal correlation present in the video signal. The first approach is the pyramidal decomposition of the Motion Compensated Frame Difference (MCFD) signal in the frequency domain and the subsequent coding by unbalanced Tree Structured Vector Quantizers (TSVQ) designed to match the statistics of the frequency bands. The type of TSVQ used in this study possess the advantage of low computational complexity with coding performance comparable to full-search vector quantization. The second approach is similar except that the order of motion estimation/compensation and pyramidal decomposition are interchanged.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
3 citations
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01 Mar 1984TL;DR: A suboptimal discrete cosine transform is used to encode image sub-blocks and a selective search through the code tree using a transform tree coding technique that is theoretically optimal for Gaussian sources and the squared error criterion at all nonzero rates.
Abstract: We have utilized for image coding a transform tree coding technique that is theoretically optimal for Gaussian sources and the squared error criterion at all nonzero rates. In the interest of affordable computation, we used a suboptimal discrete cosine transform to encode image sub-blocks and a selective search through the code tree. Coding simulations of a woman's face image at rates of 1.0 and 0.25 bits/pel gave good to excellent results. The rate 1.0 results were superior to previous ones using another tree coding method. Until now there have been no reported results with searched codes for images using a rate under 1.0 bit/pel. Encoding a computer-generated image source indicated SNR performance about 2dB less than the optimal SNR of the rate-distortion bound.
3 citations
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TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.
40,609 citations
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TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.
Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
33,785 citations
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01 Jan 1998
TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Abstract: Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.
17,693 citations
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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.
Abstract: Embedded zerotree wavelet (EZW) coding, introduced by Shapiro (see IEEE Trans. Signal Processing, vol.41, no.12, p.3445, 1993), is a very effective and computationally simple technique for image compression. We offer an alternative explanation of the principles of its operation, so that the reasons for its excellent performance can be better understood. These principles are partial ordering by magnitude with a set partitioning sorting algorithm, ordered bit plane transmission, and exploitation of self-similarity across different scales of an image wavelet transform. Moreover, we present a new and different implementation based on set partitioning in hierarchical trees (SPIHT), which provides even better performance than our previously reported extension of EZW that surpassed the performance of the original EZW. 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. In addition, the new coding and decoding procedures are extremely fast, and they can be made even faster, with only small loss in performance, by omitting entropy coding of the bit stream by the arithmetic code.
5,890 citations
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TL;DR: The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code.
Abstract: The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code The embedded code represents a sequence of binary decisions that distinguish an image from the "null" image Using an embedded coding algorithm, an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly Also, given a bit stream, the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream In addition to producing a fully embedded bit stream, the EZW consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or codebooks, and requires no prior knowledge of the image source The EZW algorithm is based on four key concepts: (1) a discrete wavelet transform or hierarchical subband decomposition, (2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, (3) entropy-coded successive-approximation quantization, and (4) universal lossless data compression which is achieved via adaptive arithmetic coding >
5,559 citations