Scanning and Sequential Decision Making for Multi-Dimensional Data - Part II: the Noisy Case

TL;DR

This paper investigates sequential decision making on noisy multi-dimensional data, focusing on scanning and filtering strategies, performance bounds, and the impact of sub-optimal scans, with applications in image and video processing.

Contribution

It extends previous work on noiseless data to noisy data, providing bounds on performance, excess loss analysis, and a universal prediction algorithm for noisy multidimensional arrays.

Findings

Derived bounds on optimal performance for noisy data scanning.

Quantified excess loss from sub-optimal scanning strategies.

Proposed a universal prediction algorithm for noisy random fields.

Abstract

We consider the problem of sequential decision making on 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 sequentially scanning and filtering noisy random fields. In this case, the sequential filter is given the freedom to choose the path over which it traverses the random field (e.g., noisy image or video sequence), thus it is natural to ask what is the best achievable performance and how sensitive this performance is to the choice of the scan. We formally define the problem of scanning and filtering, derive a bound on the best achievable performance and quantify the excess loss occurring when non-optimal scanners are used, compared to optimal scanning and filtering. We then discuss the problem of sequential scanning and prediction of noisy random fields. This setting is a natural model for applications such as restoration and coding of noisy images. We formally define the problem of scanning and prediction of a noisy multidimensional array and relate the optimal performance to the clean scandictability defined by Merhav and Weissman. Moreover, bounds on the excess loss due to sub-optimal scans are derived, and a universal prediction algorithm is suggested. This paper is the second part of a two-part paper. The first paper dealt with sequential decision making on noiseless data arrays, namely, when the decision maker is judged with respect to the same data array it observes.