Analysis of Variable-Length Codes for Integer Encoding in Hyperspectral Data Compression with the K²-Raster Compact Data Structure

• Main Authors: Kevin Chow, Dion Eustathios Olivier Tzamarias, Miguel Hernández-Cabronero, Ian Blanes, Joan Serra-Sagristà
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Remote Sensing
• Subjects: compact data structure; k2-raster; DACs; Elias codes; Simple9; Simple16
• Online Access: https://www.mdpi.com/2072-4292/12/12/1983

This paper examines the various variable-length encoders that provide integer encoding to hyperspectral scene data within a <inline-formula> <math display="inline"> <semantics> <msup> <mi>k</mi> <mn>2</mn> </msup> </semantics> </math> </inline-formula>-raster compact data structure. This compact data structure leads to a compression ratio similar to that produced by some of the classical compression techniques. This compact data structure also provides direct access for query to its data elements without requiring any decompression. The selection of the integer encoder is critical for obtaining a competitive performance considering both the compression ratio and access time. In this research, we show experimental results of different integer encoders such as Rice, Simple9, Simple16, PForDelta codes, and DACs. Further, a method to determine an appropriate <i>k</i> value for building a <inline-formula> <math display="inline"> <semantics> <msup> <mi>k</mi> <mn>2</mn> </msup> </semantics> </math> </inline-formula>-raster compact data structure with competitive performance is discussed.