abstract

Abstract Abstract Experimental results Experimental results The goal of this project is to examine the performance of different lossless compression algorithms and determine which one would be the most suitable for a cubeSat based communication system. As the cubeSat based communication system has both high data collection potential and relatively low data transmission throughput, optimal compression is a key performance bottleneck for the overall system performance. We select an optimal subset of the images that are generated using the IR camera mounted on a cubeSat and compress these subset of images. The compression algorithms we investigated and tested are run- length encoding, difference encoding, and Huffman coding. Initially, we implement these algorithms in MATLAB. Later, we will use the datasets developed in the first part of the cubeSat project. Alexander Gu, Phani Chevali, Ed Richter, and Arye Nehorai Department of Electrical and Systems Engineering Overview Overview Compression algorithms Compression algorithms Fixed byte- Fixed byte- length length Examples of images processed Examples of images processed Goal Goal Develop an optimal lossless compression strategy for use on the cubeSat platform Approach Approach • Mathematical optimization of different compression algorithms using modeled image data. Applications Applications mobile data collecting platforms that are constrained by the communication bandwidth. Background Background Figure: Photograph of a cubeSat micro-satellite (Source: Stensat Group, LLC) CubeSat based communication system : Part II-Data compression for CubeSat based communication system : Part II-Data compression for IR images IR images Difference Encoding Run-length encoding (RLE) References References Variable run-length encoding (VRLE) •Rosettacode.org • Mordecai J. Golin, Claire Kenyon, Neal E. Young "Huffman coding with unequal letter costs" (PDF), STOC 2002: 785-791 •Wikipedia.com • Korn, D.G.; Vo, K.P. (1995), B. Krishnamurthy, ed., Vdelta: Differencing and Compression, Practical Reusable Unix Software, John Wiley & Sons Variable byte Variable byte length length Huffman coding assigns varying bit-length codes to each symbol based on the symbol frequency. In the above example the four symbols are a1,a2,a3,a4. based on the sorting algorithm their assigned byte-codes are 0, 10, 110, 111 respectively. Huffman coding cubeSat platform 10cm 10cm 10cm WWWWRRRWWWWRW W4R3W4R1W1 ORIGINAL DATA: COMPRESSED DATA: Stores one data element followed by one run-length element, representing the number of immediate repetitions. WWWWRRRWWWWRW W14R13W14R0W0 ORIGINAL DATA: COMPRESSED DATA: Similar method as RLE. Except the first bit is reserved for indicating whether a run is present or not. WWWWRRRWWWWRW W000-5005000-55 ORIGINAL DATA: COMPRESSED DATA: * * This result is calculated using the ascii representations of W and R Stores one data element followed by a difference value representing the difference between the two data elements Figure: Example of Huffman coding. Alessio Damato 18 May 2007(2007-05-18) Original image Decompressed image Compresse d Data Compression Decompressi on Lossless Transmission Compresse d Data Compression Decompressi on Color image results IR image results

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CubeSat based communication system : Part II-Data compression for IR images. Alexander Gu, Phani Chevali, Ed Richter, and Arye Nehorai Department of Electrical and Systems Engineering. Compression algorithms. Abstract. Examples of images processed. - PowerPoint PPT Presentation

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AbstractAbstract

Experimental resultsExperimental results

The goal of this project is to examine the performance of different lossless compression algorithms and determine which one would be the most suitable for a cubeSat based communication system. As the cubeSat based communication system has both high data collection potential and relatively low data transmission throughput, optimal compression is a key performance bottleneck for the overall system performance. We select an optimal subset of the images that are generated using the IR camera mounted on a cubeSat and compress these subset of images. The compression algorithms we investigated and tested are run-length encoding, difference encoding, and Huffman coding. Initially, we implement these algorithms in MATLAB. Later, we will use the datasets developed in the first part of the cubeSat project.

Alexander Gu, Phani Chevali, Ed Richter, and Arye NehoraiDepartment of Electrical and Systems Engineering

OverviewOverview

Compression algorithmsCompression algorithms

Fixed byte-lengthFixed byte-length

Examples of images processedExamples of images processed

GoalGoal

Develop an optimal lossless compression strategy for use on the cubeSat platform

ApproachApproach

• Mathematical optimization of different compression algorithms using modeled image data.

ApplicationsApplications

mobile data collecting platforms that are constrained by the communication bandwidth.

BackgroundBackground

Figure: Photograph of a cubeSat micro-satellite (Source: Stensat Group, LLC)

CubeSat based communication system : Part II-Data compression for IR imagesCubeSat based communication system : Part II-Data compression for IR images

Difference Encoding

Run-length encoding (RLE)

ReferencesReferences

Variable run-length encoding (VRLE)

•Rosettacode.org

• Mordecai J. Golin, Claire Kenyon, Neal E. Young "Huffman coding with unequal letter costs" (PDF), STOC 2002: 785-791

•Wikipedia.com

• Korn, D.G.; Vo, K.P. (1995), B. Krishnamurthy, ed., Vdelta: Differencing and Compression, Practical Reusable Unix Software, John Wiley & Sons

Variable byte lengthVariable byte length

Huffman coding assigns varying bit-length codes to each symbol based on the symbol frequency. In the above

example the four symbols are a1,a2,a3,a4. based on the sorting algorithm their assigned byte-codes are 0, 10, 110,

111 respectively.