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Efficient multiscale regularization with applications to the computation of optical flow
 IEEE Trans. Image Process
, 1994
"... AbsfruetA new approach to regularization methods for image processing is introduced and developed using as a vehicle the problem of computing dense optical flow fields in an image sequence. Standard formulations of this problem require the computationally intensive solution of an elliptic partial d ..."
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Cited by 103 (34 self)
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AbsfruetA new approach to regularization methods for image processing is introduced and developed using as a vehicle the problem of computing dense optical flow fields in an image sequence. Standard formulations of this problem require the computationally intensive solution of an elliptic partial differential equation that arises from the often used “smoothness constraint” ’yl”. regularization. The interpretation of the smoothness constraint is utilized as a “fractal prior ” to motivate regularization based on a recently introduced class of multiscale stochastic models. The solution of the new problem formulation is computed with an efficient multiscale algorithm. Experiments on several image sequences demonstrate the substantial computational savings that can be achieved due to the fact that the algorithm is noniterative and in fact has a per pixel computational complexity that is independent of image size. The new approach also has a number of other important advantages. Specifically, multiresolution flow field estimates are available, allowing great flexibility in dealing with the tradeoff between resolution and accuracy. Multiscale error covariance information is also available, which is of considerable use in assessing the accuracy of the estimates. In particular, these error statistics can be used as the basis for a rational procedure for determining the spatiallyvarying optimal reconstruction resolution. Furthermore, if there are compelling reasons to insist upon a standard smoothness constraint, our algorithm provides an excellent initialization for the iterative algorithms associated with the smoothness constraint problem formulation. Finally, the usefulness of our approach should extend to a wide variety of illposed inverse problems in which variational techniques seeking a “smooth ” solution are generally Used. I.
Motion estimation techniques for digital TV: A review and a new contribution
 Proc. IEEE
, 1995
"... The key to high performance in image sequence coding lies in an efficient reduction of the temporal redundancies. For this purpose, motion estimation and compensation techniques have been suc cessfully applied. This paper studies motion estimation algorithms in the context of first generation coding ..."
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Cited by 89 (1 self)
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The key to high performance in image sequence coding lies in an efficient reduction of the temporal redundancies. For this purpose, motion estimation and compensation techniques have been suc cessfully applied. This paper studies motion estimation algorithms in the context of first generation coding techniques commonly used in digital TV. In this framework, estimating the motion in the scene is not an intrinsic goal. Motion estimation should indeed provide good temporal prediction and simultaneously require low overhead information. More specifically, the aim is to minimize globally the bandwidth corresponding to both the prediction error information and the motion parameters. This paper first clarifies the notion of motion, reviews classical motion estimation tech niques, and outlines new perspectives. Block matching techniques are shown to be the most appropriate in the framework of first generation coding. To overcome the drawbacks characteristic of most block matching techniques, this paper proposes a new locally adaptive multigrid block matching motion estimation technique. This algorithm has been designed taking into account the above aims. It leads to a robust motion field estimation, precise prediction along moving edges and a decreased amount of side information in uniform areas. Furthermore, the algorithm controls the accuracy of the motion estimation procedure in order to optimally balance the amount of information corresponding to the prediction error and to the motion parameters. Experimental results show that the technique results in greatly enhanced visual quality and significant saving in terms of bit rate when compared to classical block matching techniques. I.
Motion Picture Restoration
, 1993
"... This dissertation presents algorithms for restoring some of the major corruptions observed in archived film or video material. The two principal problems of impulsive distortion (Dirt and Sparkle or Blotches) and noise degradation are considered. There is also an algorithm for suppressing the inter ..."
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Cited by 64 (11 self)
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This dissertation presents algorithms for restoring some of the major corruptions observed in archived film or video material. The two principal problems of impulsive distortion (Dirt and Sparkle or Blotches) and noise degradation are considered. There is also an algorithm for suppressing the interline jitter common in images decoded from noisy video signals. In the case of noise reduction and Blotch removal the thesis considers image sequences to be three dimensional signals involving evolution of features in time and space. This is necessary if any process presented is to show an improvement over standard twodimensional techniques. It is important to recognize that consideration of image sequences must involve an appreciation of the problems incurred by the motion of objects in the scene. The most obvious implication is that due to motion, useful three dimensional processing does not necessarily proceed in a direction `orthogonal' to the image frames. Therefore, attention is giv...
Image Processing with Multiscale Stochastic Models
, 1993
"... In this thesis, we develop image processing algorithms and applications for a particular class of multiscale stochastic models. First, we provide background on the model class, including a discussion of its relationship to wavelet transforms and the details of a twosweep algorithm for estimation. A ..."
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Cited by 31 (3 self)
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In this thesis, we develop image processing algorithms and applications for a particular class of multiscale stochastic models. First, we provide background on the model class, including a discussion of its relationship to wavelet transforms and the details of a twosweep algorithm for estimation. A multiscale model for the error process associated with this algorithm is derived. Next, we illustrate how the multiscale models can be used in the context of regularizing illposed inverse problems and demonstrate the substantial computational savings that such an approach offers. Several novel features of the approach are developed including a technique for choosing the optimal resolution at which to recover the object of interest. Next, we show that this class of models contains other widely used classes of statistical models including 1D Markov processes and 2D Markov random fields, and we propose a class of multiscale models for approximately representing Gaussian Markov random fields...
A Novel Filter for BlockBased Object Motion Estimation
"... and other research outputs A novel filter for blockbased motion estimation ..."
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Cited by 1 (0 self)
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and other research outputs A novel filter for blockbased motion estimation
Fast BlockBased True Motion Estimation Using Distance Dependent Thresholds
"... A fast motion estimation algorithm, called distancedependent thresholding search (DTS), is presented for blockbased true motion estimation applications, and introduces the novel concept of variable distance dependent thresholds. The performance of the DTS algorithm is analysed and quantitatively c ..."
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Cited by 1 (1 self)
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A fast motion estimation algorithm, called distancedependent thresholding search (DTS), is presented for blockbased true motion estimation applications, and introduces the novel concept of variable distance dependent thresholds. The performance of the DTS algorithm is analysed and quantitatively compared with both the traditional and exhaustive fullsearch (FS) technique, and the computationally faster, nonexhaustive threestepsearch (TSS) algorithm. Experimental results show that by applying an appropriate threshold function, the DTS algorithm not only matches the speed of the TSS algorithm, but both retains a block distortion error comparable to the global minimum produced by the FS algorithm, and avoids the problem of identifying a large number of spurious motion vectors in the search process. ACM Classification: I.4 (Image processing and computer vision) 1.
Chapter V Very Low Bitrate Video Coding
"... This chapter presents a contemporary review of the various different strategies available to facilitate Very Low BitRate (VLBR) coding for video communications over mobile and fixed transmission channels as well as the Internet. VLBR media is typically classified as having a bit rate between 8 and ..."
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This chapter presents a contemporary review of the various different strategies available to facilitate Very Low BitRate (VLBR) coding for video communications over mobile and fixed transmission channels as well as the Internet. VLBR media is typically classified as having a bit rate between 8 and 64 Kbps. Techniques that are analyzed include Vector Quantization, various parametric modelbased representations, the Discrete Wavelet and Cosine Transforms, and fixed and arbitrary shaped patternbased coding. In addition to discussing the underlying theoretical principles and relevant features of each approach, the chapter also examines their benefits and disadvantages, together with some of the major challenges that remain to be solved. The chapter concludes by providing some judgments on the likely focus of future research in the VLBR coding field.
Motion estimation
"... In image sequences, motion of objects and camera (pan, rotation, zoom) is source of temporal variations. Parameters of threedimensional motion (rotation, translation) play an important role in image motion modeling. If these parameters are known precisely, the object motion can be accurately predic ..."
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In image sequences, motion of objects and camera (pan, rotation, zoom) is source of temporal variations. Parameters of threedimensional motion (rotation, translation) play an important role in image motion modeling. If these parameters are known precisely, the object motion can be accurately predicted and used in such problems as image compression, segmentation, denoising and highresolution processing. The 3D relative movement of objects and camera induces 2D motion on the image plane via a suitable projection system. The corresponding 2D projection is called apparent motion or optical flow. Itisthisparticular projected motion that needs to be recovered from intensity variation information of a video sequence. Naturally, these data are oftendegradedbynoiseanddisturbsofdifferent nature, and the motion estimation basic problem is to reconstruct the correct 2D movement in the image plane. The motion field cannot be computed locally on a point in the image, independently of neighboring points without introducing additional constraints, because recovering the velocity at each image point means to calculate its two components while the change in image brightness at a point in the image plane due to motion yields only one constraint. A