RBF based spatio-temporal representation technique for video compression
12 Dec 2010-pp 485-489
TL;DR: This paper performs Oct-Tree Decomposition on a video stack, followed by parameter extraction using Radial Basis Function Networks (RBFN) to achieve exceptionally high compression ratios, even higher than the state of art H.264 codec.
Abstract: Parametric coding is a technique in which data is processed to extract meaningful information and then representing it compactly using appropriate parameters. Parametric Coding exploits redundancy in information to provide a very compact representation and thus achieves very high compression ratios. However, this is achieved at the cost of higher computation complexity. This disadvantage is now being offset by the availability of high speed processors, thus making it possible to exploit the high compression ratios of the parametric video coding techniques. In this paper a novel idea for efficient parametric representation of video is proposed. We perform Oct-Tree Decomposition on a video stack, followed by parameter extraction using Radial Basis Function Networks (RBFN) to achieve exceptionally high compression ratios, even higher than the state of art H.264 codec. The proposed technique exploits spatial-temporal redundancy and therefore inherently achieves multiframe prediction.
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TL;DR: Regularization networks are mathematically related to the radial basis functions, mainly used for strict interpolation tasks as mentioned in this paper, and two extensions of the regularization approach are presented, along with the approach's corrections to splines, regularization, Bayes formulation, and clustering.
Abstract: The problem of the approximation of nonlinear mapping, (especially continuous mappings) is considered. Regularization theory and a theoretical framework for approximation (based on regularization techniques) that leads to a class of three-layer networks called regularization networks are discussed. Regularization networks are mathematically related to the radial basis functions, mainly used for strict interpolation tasks. Learning as approximation and learning as hypersurface reconstruction are discussed. Two extensions of the regularization approach are presented, along with the approach's corrections to splines, regularization, Bayes formulation, and clustering. The theory of regularization networks is generalized to a formulation that includes task-dependent clustering and dimensionality reduction. Applications of regularization networks are discussed. >
3,595 citations
"RBF based spatio-temporal represent..." refers background in this paper
...RBFN are known to provide universal approximations on a compact subset of [[8]]....
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TL;DR: A new approach to the detection and classification of scene breaks in video sequences that can withstand compression artifacts such as those introduced by JPEG and MPEG, even at very high compression rates.
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582 citations
"RBF based spatio-temporal represent..." refers background in this paper
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TL;DR: It is described how epitomes can be used to model video data and significant computational speedups that can be incorporated into the epitome inference and learning algorithm are described.
Abstract: Recently, "epitomes" were introduced as patch-based probability models that are learned by compiling together a large number of examples of patches from input images. In this paper, we describe how epitomes can be used to model video data and we describe significant computational speedups that can be incorporated into the epitome inference and learning algorithm. In the case of videos, epitomes are estimated so as to model most of the small space-time cubes from the input data. Then, the epitome can be used for various modeling and reconstruction tasks, of which we show results for video super-resolution, video interpolation, and object removal. Besides computational efficiency, an interesting advantage of the epitome as a representation is that it can be reliably estimated even from videos with large amounts of missing data. We illustrate this ability on the task of reconstructing the dropped frames in video broadcast using only the degraded video and also in denoising a severely corrupted video.
128 citations
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TL;DR: This paper describes how epitomes can be used to model video data and it describes significant computational speedups that can be incorporated into the epitome inference and learning algorithm.
Abstract: Recently, "epitomes" were introduced as patch-based probability models that are learned by compiling together a large number of examples of patches from input images. In this paper, we describe how epitomes can be used to model video data and we describe significant computational speedups that can be incorporated into the epitome inference and learning algorithm. In the case of videos, epitomes are estimated so as to model most of the small space-time cubes from the input data. Then, the epitome can be used for various modeling and reconstruction tasks, of which we show results for video super-resolution, video interpolation, and object removal. Besides computational efficiency, an interesting advantage of the epitome as a representation is that it can be reliably estimated even from videos with large amounts of missing data. We illustrate this ability on the task of reconstructing the dropped frames in video broadcast using only the degraded video.
116 citations