Journal Article•
Proceedings of the IEEE International Conference on Image Processing
About: This article is published in The Institute of Electrical and Electronics Engineers.The article was published on 1996-01-01 and is currently open access. It has received 107 citations till now. The article focuses on the topics: Image processing.
Citations
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01 Jun 1998
TL;DR: The reliability of data-embedding procedures and their ability to deliver new services such as viewing a movie in a given rated version from a single multicast stream are discussed.
Abstract: We review developments in transparent data embedding and watermarking for audio, image, and video. Data-embedding and watermarking algorithms embed text, binary streams, audio, image, or video in a host audio, image, or video signal. The embedded data are perceptually inaudible or invisible to maintain the quality of the source data. The embedded data can add features to the host multimedia signal, e.g., multilingual soundtracks in a movie, or provide copyright protection. We discuss the reliability of data-embedding procedures and their ability to deliver new services such as viewing a movie in a given rated version from a single multicast stream. We also discuss the issues and problems associated with copy and copyright protection and assess the viability of current watermarking algorithms as a means for protecting copyrighted data.
1,023 citations
TL;DR: Several new results are proved which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms, and introduce a new algorithm, resulting from using bounds for nonconvex regularizers, which confirms the superior performance of this method, when compared to the one based on quadratic majorization.
Abstract: Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to very high-dimensional optimization problems involving the following difficulties: the non-Gaussian (heavy-tailed) wavelet priors lead to objective functions which are nonquadratic, usually nondifferentiable, and sometimes even nonconvex; the presence of the convolution operator destroys the separability which underlies the simplicity of wavelet-based denoising. This paper presents a unified view of several recently proposed algorithms for handling this class of optimization problems, placing them in a common majorization-minimization (MM) framework. One of the classes of algorithms considered (when using quadratic bounds on nondifferentiable log-priors) shares the infamous ?singularity issue? (SI) of ?iteratively re weighted least squares? (IRLS) algorithms: the possibility of having to handle infinite weights, which may cause both numerical and convergence issues. In this paper, we prove several new results which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms. Exploiting the unified MM perspective, we introduce a new algorithm, resulting from using bounds for nonconvex regularizers; the experiments confirm the superior performance of this method, when compared to the one based on quadratic majorization. Finally, an experimental comparison of the several algorithms, reveals their relative merits for different standard types of scenarios.
568 citations
Cites background or methods from "Proceedings of the IEEE Internation..."
...We show that the methods independently introduced by several authors [18], [23], [24], [27], [28], [40], [49], [50] can all be seen as MM algorithms based on a separable quadratic majorizer on the log-likelihood....
[...]
...…formulations of image deconvolution under waveletbased priors lead to very large scale optimization problems where the objective function has two terms: a quadratic log-likelihood (or data discrepancy) term plus a (usually non quadratic) log-prior (also known as regularizer of penalty function)....
[...]
24 Mar 2010
TL;DR: Block-based random image sampling is coupled with a projection-driven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image, yielding images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation.
Abstract: Recent years have seen significant interest in the paradigm of compressed sensing (CS) which permits, under certain conditions, signals to be sampled at sub-Nyquist rates via linear projection onto a random basis while still enabling exact reconstruction of the original signal. As applied to 2D images, however, CS faces several challenges including a computationally expensive reconstruction process and huge memory required to store the random sampling operator. Recently, several fast algorithms have been developed for CS reconstruction, while the latter challenge was addressed by Gan using a block-based sampling operation as well as projection-based Landweber iterations to accomplish fast CS reconstruction while simultaneously imposing smoothing with the goal of improving the reconstructed-image quality by eliminating blocking artifacts. In this technique, smoothing is achieved by interleaving Wiener filtering with the Landweber iterations, a process facilitated by the relative simple implementation of the Landweber algorithm. In this work, we adopt Gan's basic framework of block-based CS sampling of images coupled with iterative projection-based reconstruction with smoothing. Our contribution lies in that we cast the reconstruction in the domain of recent transforms that feature a highly directional decomposition. These transforms---specifically, contourlets and complex-valued dual-tree wavelets---have shown promise to overcome deficiencies of widely-used wavelet transforms in several application areas. In their application to iterative projection-based CS recovery, we adapt bivariate shrinkage to their directional decomposition structure to provide sparsity-enforcing thresholding, while a Wiener-filter step encourages smoothness of the result. In experimental simulations, we find that the proposed CS reconstruction based on directional transforms outperforms equivalent reconstruction using common wavelet and cosine transforms. Additionally, the proposed technique usually matches or exceeds the quality of total-variation (TV) reconstruction, a popular approach to CS recovery for images whose gradient-based operation also promotes smoothing but runs several orders of magnitude slower than our proposed algorithm.
387 citations
30 Aug 1999
TL;DR: This paper presents a mechanism for using layered video in the context of unicast congestion control, which adds and drops layers of the video stream to perform long-term coarse-grain adaptation, while using a TCP-friendly congestion control mechanism to react to congestion on very short timescales.
Abstract: Streaming audio and video applications are becoming increasingly popular on the Internet, and the lack of effective congestion control in such applications is now a cause for significant concern. The problem is one of adapting the compression without requiring video-servers to re-encode the data, and fitting the resulting stream into the rapidly varying available bandwidth. At the same time, rapid fluctuations in quality will be disturbing to the users and should be avoided.In this paper we present a mechanism for using layered video in the context of unicast congestion control. This quality adaptation mechanism adds and drops layers of the video stream to perform long-term coarse-grain adaptation, while using a TCP-friendly congestion control mechanism to react to congestion on very short timescales. The mismatches between the two timescales are absorbed using buffering at the receiver. We present an efficient-scheme for the distribution of buffering among the active layers. Our scheme allows the server to trade short-term improvement for long-term smoothing of quality. We discuss the issues involved in implementing and tuning such a mechanism, and present our simulation results.
242 citations
Cites background from "Proceedings of the IEEE Internation..."
...One may re-quantizing stored encodings on-the-fly based on network feedback[2, 15, 18]....
[...]
TL;DR: In this article, the authors revisited the problem of detecting ringdown waveforms and estimating the source parameters, considering both LISA and Earth-based interferometers, and showed that the single-mode ringdown templates presently used for ringdown searches in the LIGO data stream can produce a significant event loss (>10% for all detectors in a large interval of black hole masses) and very large estimation errors on the black hole's mass and spin.
Abstract: Using recent results from numerical relativity simulations of nonspinning binary black hole mergers, we revisit the problem of detecting ringdown waveforms and of estimating the source parameters, considering both LISA and Earth-based interferometers. We find that Advanced LIGO and EGO could detect intermediate-mass black holes of mass up to {approx}10{sup 3}M{sub {center_dot}} out to a luminosity distance of a few Gpc. For typical multipolar energy distributions, we show that the single-mode ringdown templates presently used for ringdown searches in the LIGO data stream can produce a significant event loss (>10% for all detectors in a large interval of black hole masses) and very large parameter estimation errors on the black hole's mass and spin. We estimate that more than {approx}10{sup 6} templates would be needed for a single-stage multimode search. Therefore, we recommend a ''two-stage'' search to save on computational costs: single-mode templates can be used for detection, but multimode templates or Prony methods should be used to estimate parameters once a detection has been made. We update estimates of the critical signal-to-noise ratio required to test the hypothesis that two or more modes are present in the signal and to resolve their frequencies, showing that second-generation Earth-based detectors andmore » LISA have the potential to perform no-hair tests.« less
174 citations
Cites background or methods from "Proceedings of the IEEE Internation..."
...[43] http://einstein.phys.uwm.edu/ [44] M. Shahram and P. Milanfar, IEEE Transactions on Signal Processing 53, 2579 (2005)....
[...]
...(B2) 4 See also the work by Milanfar and Shahram [44, 45]....
[...]
...4 See also the work by Milanfar and Shahram [44, 45]....
[...]
...The choice of γ is motivated by the level of tolerable false-positive rate [44, 45]....
[...]
...We follow Milanfar and Shahram [44, 45] and consider the generalized likelihood ratio test, which proceeds by computing first the maximum likelihood (ML) estimates of the unknown parameters....
[...]
References
More filters
01 Jun 1998
TL;DR: The reliability of data-embedding procedures and their ability to deliver new services such as viewing a movie in a given rated version from a single multicast stream are discussed.
Abstract: We review developments in transparent data embedding and watermarking for audio, image, and video. Data-embedding and watermarking algorithms embed text, binary streams, audio, image, or video in a host audio, image, or video signal. The embedded data are perceptually inaudible or invisible to maintain the quality of the source data. The embedded data can add features to the host multimedia signal, e.g., multilingual soundtracks in a movie, or provide copyright protection. We discuss the reliability of data-embedding procedures and their ability to deliver new services such as viewing a movie in a given rated version from a single multicast stream. We also discuss the issues and problems associated with copy and copyright protection and assess the viability of current watermarking algorithms as a means for protecting copyrighted data.
1,023 citations
TL;DR: Several new results are proved which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms, and introduce a new algorithm, resulting from using bounds for nonconvex regularizers, which confirms the superior performance of this method, when compared to the one based on quadratic majorization.
Abstract: Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to very high-dimensional optimization problems involving the following difficulties: the non-Gaussian (heavy-tailed) wavelet priors lead to objective functions which are nonquadratic, usually nondifferentiable, and sometimes even nonconvex; the presence of the convolution operator destroys the separability which underlies the simplicity of wavelet-based denoising. This paper presents a unified view of several recently proposed algorithms for handling this class of optimization problems, placing them in a common majorization-minimization (MM) framework. One of the classes of algorithms considered (when using quadratic bounds on nondifferentiable log-priors) shares the infamous ?singularity issue? (SI) of ?iteratively re weighted least squares? (IRLS) algorithms: the possibility of having to handle infinite weights, which may cause both numerical and convergence issues. In this paper, we prove several new results which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms. Exploiting the unified MM perspective, we introduce a new algorithm, resulting from using bounds for nonconvex regularizers; the experiments confirm the superior performance of this method, when compared to the one based on quadratic majorization. Finally, an experimental comparison of the several algorithms, reveals their relative merits for different standard types of scenarios.
568 citations
24 Mar 2010
TL;DR: Block-based random image sampling is coupled with a projection-driven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image, yielding images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation.
Abstract: Recent years have seen significant interest in the paradigm of compressed sensing (CS) which permits, under certain conditions, signals to be sampled at sub-Nyquist rates via linear projection onto a random basis while still enabling exact reconstruction of the original signal. As applied to 2D images, however, CS faces several challenges including a computationally expensive reconstruction process and huge memory required to store the random sampling operator. Recently, several fast algorithms have been developed for CS reconstruction, while the latter challenge was addressed by Gan using a block-based sampling operation as well as projection-based Landweber iterations to accomplish fast CS reconstruction while simultaneously imposing smoothing with the goal of improving the reconstructed-image quality by eliminating blocking artifacts. In this technique, smoothing is achieved by interleaving Wiener filtering with the Landweber iterations, a process facilitated by the relative simple implementation of the Landweber algorithm. In this work, we adopt Gan's basic framework of block-based CS sampling of images coupled with iterative projection-based reconstruction with smoothing. Our contribution lies in that we cast the reconstruction in the domain of recent transforms that feature a highly directional decomposition. These transforms---specifically, contourlets and complex-valued dual-tree wavelets---have shown promise to overcome deficiencies of widely-used wavelet transforms in several application areas. In their application to iterative projection-based CS recovery, we adapt bivariate shrinkage to their directional decomposition structure to provide sparsity-enforcing thresholding, while a Wiener-filter step encourages smoothness of the result. In experimental simulations, we find that the proposed CS reconstruction based on directional transforms outperforms equivalent reconstruction using common wavelet and cosine transforms. Additionally, the proposed technique usually matches or exceeds the quality of total-variation (TV) reconstruction, a popular approach to CS recovery for images whose gradient-based operation also promotes smoothing but runs several orders of magnitude slower than our proposed algorithm.
387 citations
30 Aug 1999
TL;DR: This paper presents a mechanism for using layered video in the context of unicast congestion control, which adds and drops layers of the video stream to perform long-term coarse-grain adaptation, while using a TCP-friendly congestion control mechanism to react to congestion on very short timescales.
Abstract: Streaming audio and video applications are becoming increasingly popular on the Internet, and the lack of effective congestion control in such applications is now a cause for significant concern. The problem is one of adapting the compression without requiring video-servers to re-encode the data, and fitting the resulting stream into the rapidly varying available bandwidth. At the same time, rapid fluctuations in quality will be disturbing to the users and should be avoided.In this paper we present a mechanism for using layered video in the context of unicast congestion control. This quality adaptation mechanism adds and drops layers of the video stream to perform long-term coarse-grain adaptation, while using a TCP-friendly congestion control mechanism to react to congestion on very short timescales. The mismatches between the two timescales are absorbed using buffering at the receiver. We present an efficient-scheme for the distribution of buffering among the active layers. Our scheme allows the server to trade short-term improvement for long-term smoothing of quality. We discuss the issues involved in implementing and tuning such a mechanism, and present our simulation results.
242 citations
TL;DR: In this article, the authors revisited the problem of detecting ringdown waveforms and estimating the source parameters, considering both LISA and Earth-based interferometers, and showed that the single-mode ringdown templates presently used for ringdown searches in the LIGO data stream can produce a significant event loss (>10% for all detectors in a large interval of black hole masses) and very large estimation errors on the black hole's mass and spin.
Abstract: Using recent results from numerical relativity simulations of nonspinning binary black hole mergers, we revisit the problem of detecting ringdown waveforms and of estimating the source parameters, considering both LISA and Earth-based interferometers. We find that Advanced LIGO and EGO could detect intermediate-mass black holes of mass up to {approx}10{sup 3}M{sub {center_dot}} out to a luminosity distance of a few Gpc. For typical multipolar energy distributions, we show that the single-mode ringdown templates presently used for ringdown searches in the LIGO data stream can produce a significant event loss (>10% for all detectors in a large interval of black hole masses) and very large parameter estimation errors on the black hole's mass and spin. We estimate that more than {approx}10{sup 6} templates would be needed for a single-stage multimode search. Therefore, we recommend a ''two-stage'' search to save on computational costs: single-mode templates can be used for detection, but multimode templates or Prony methods should be used to estimate parameters once a detection has been made. We update estimates of the critical signal-to-noise ratio required to test the hypothesis that two or more modes are present in the signal and to resolve their frequencies, showing that second-generation Earth-based detectors andmore » LISA have the potential to perform no-hair tests.« less
174 citations