Feasibility study for lossless data compression of remotely sensed images using difference-mapped shift-extended Huffman coding

Sandeep Jaggi

doi:10.1117/12.138243

9 July 1992 Feasibility study for lossless data compression of remotely sensed images using difference-mapped shift-extended Huffman coding

Sandeep Jaggi

Author Affiliations +

Proceedings Volume 1699, Signal Processing, Sensor Fusion, and Target Recognition; (1992) https://doi.org/10.1117/12.138243
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

This report describes a study to investigate the lossless compression of multispectral visible and thermal imagery data. The imagery is obtained from remotely sensed data acquired from airborne scanners maintained and operated by NASA at the Stennis Space Center. The aim is to determine the degree of compression possible and then implement algorithms that perform lossless data compression on images. The application of this technique lay in further compressing data that has already been subjected to a lossy technique called Vector Quantization. The output data from the vector quantization algorithm was compressed without any further increase in the RMS error. Initially, the data was mapped to a difference transform. This transformed image was then converted into symbols using shift-extended codes of a specific bit-size. These symbols were then coded using Huffman coding. The complexity of the implementation increases with the bit-size. Hence the effect of the bit-size on the compression ratio was also examined. The data from a NASA 6 channel sensor called Thermal Infrared Multispectral Scanner (TIMS) resulted in additional compression of 5.33 (for an image vector quantized with four codewords) to 1.28 (for an image vector quantized with 128 codewords). The data from a 7 channel sensor called Calibrated Airborne Multispectral Scanner (CAMS) resulted in additional compression of 7 (for an image vector quantized with four codewords) to 2.22 (for an image vector quantized with 128 codewords). The algorithms were implemented in software and interfaced with the help of dedicated image processing boards to an 80386 PC compatible computer. Modules were developed for the task of image compression and image analysis. These modules are very general in nature and are thus capable of analyzing any sets or types of images or voluminous data sets. Also, supporting software to perform image processing for visual display and interpretation of the compressed/classified images was developed.

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Sandeep Jaggi "Feasibility study for lossless data compression of remotely sensed images using difference-mapped shift-extended Huffman coding", Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); https://doi.org/10.1117/12.138243

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KEYWORDS

Image compression

Scanners

Data acquisition

Data compression

Image processing

Quantization

Sensors

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