Scanning and Sequential Decision Making for Multidimensional Data–Part I: The Noiseless Case
نویسندگان
چکیده
منابع مشابه
Scanning and Sequential Decision Making for Multi-Dimensional Data - Part I: the Noiseless Case
We investigate the problem of scanning and prediction (“scandiction”, for short) of multidimensional data arrays. This problem arises in several aspects of image and video processing, such as predictive coding, for example, where an image is compressed by coding the error sequence resulting from scandicting it. Thus, it is natural to ask what is the optimal method to scan and predict a given im...
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We investigate the problem of scanning and prediction (“scandiction”, for short) of multidimensional data arrays. This problem arises in several aspects of image and video processing, such as predictive coding, for example, where an image is compressed by coding the error sequence resulting from scandicting it. Thus, it is natural to ask what is the optimal method to scan and predict a given im...
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We investigate several problems in scanning of multidimensional data arrays, such as universal scanning and prediction (“scandiction”, for short), and scandiction of noisy data arrays. These problems arise in several aspects of image and video processing, such as predictive coding, filtering and denoising. In predictive coding of images, for example, an image is compressed by coding the predict...
متن کامل1 1 Se p 20 06 Universal Scanning and Sequential Decision Making for Multi - Dimensional Data - Part I : the Noiseless Case ∗
We investigate the problem of scanning and prediction (“scandiction”, for short) of multidimensional data arrays. This problem arises in several aspects of image and video processing, such as predictive coding, for example, where an image is compressed by coding the error sequence resulting from scandicting it. Thus, it is natural to ask what is the optimal method to scan and predict a given im...
متن کاملScanning and Sequential Decision Making for Multi-Dimensional Data - Part II: the Noisy Case
We consider the problem of sequential decision making for random fields corrupted by noise. In this scenario, the decision maker observes a noisy version of the data, yet judged with respect to the clean data. In particular, we first consider the problem of scanning and sequentially filtering noisy random fields. In this case, the sequential filter is given the freedom to choose the path over w...
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ژورنال
عنوان ژورنال: IEEE Transactions on Information Theory
سال: 2007
ISSN: 0018-9448
DOI: 10.1109/tit.2007.903117