Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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 image, what is the resulting minimum prediction loss, and whether there exist speciflc scandiction schemes which are universal in some sense. Speciflcally,  investigate the following problems: First, modeling the data array as a random fleld,  wish to examine whether there exists a scandiction scheme which is independent of the fleld’s distribution, yet asymptotically achieves the same performance as if this distribution was known. This question is ansred in the a‐rmative for the set of all spatially stationary random flelds and under mild conditions on the loss function.  then discuss the scenario where a non-optimal scanning order is used, yet accompanied by an optimal predictor, and derive bounds on the excess loss compared to optimal scanning and prediction.  is the flrst part of a two-part paper on sequential decision making for multidimensional data. It deals with clean, noiseless data arrays. The second part [1] deals with
['Asaf Cohen', 'Neri Merhav', 'Tsachy Weissman']
I am writing a research paper on a idea Universal Scanning and Sequential Decision Making for Multi-Dimensional Data - Part I: the Noiseless Case
[{"from": "human", "value": "I am writing a research paper on a idea Universal Scanning and Sequential Decision Making for Multi-Dimensional Data - Part I: the Noiseless Case"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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 image, what is the resulting minimum prediction loss, and whether there exist speciflc scandiction schemes which are universal in some sense. Speciflcally,  investigate the following problems: First, modeling the data array as a random fleld,  wish to examine whether there exists a scandiction scheme which is independent of the fleld\u2019s distribution, yet asymptotically achieves the same performance as if this distribution was known. This question is ansred in the a\u2010rmative for the set of all spatially stationary random flelds and under mild conditions on the loss function.  then discuss the scenario where a non-optimal scanning order is used, yet accompanied by an optimal predictor, and derive bounds on the excess loss compared to optimal scanning and prediction.  is the flrst part of a two-part paper on sequential decision making for multidimensional data. It deals with clean, noiseless data arrays. The second part [1] deals with"}]