Abstract
This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k 2 -raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k 2 -raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding.
Highlights
Compact data structures [1] are examined in this paper as they can provide real-time processing and compression of remote sensing images
We explore the use of a predictor and a differential encoder as preprocessing on the compact data structure to see if it can provide us with further bit-rate reduction
We will use k2 -raster from Ladra et al [20], a concept which was developed from k2 -tree, a type of compact data structure, as well as the idea of using recursive decomposition of quadtrees
Summary
Compact data structures [1] are examined in this paper as they can provide real-time processing and compression of remote sensing images. These structures are stored in reduced space in a compact form. Functions can be used to access and query each datum or groups of data directly in an efficient manner without an initial full decompression. This compact data should have a size which is close to the information-theoretic minimum. Jacobson’s paper is often considered the starting point of this topic Since it has gained more attention and a number of research papers have been published. Research on algorithms such as FM-index [4,5] and Burrows-Wheeler transform [6]
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