Burrows Wheeler Transform In Image Compression

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Burrows Wheeler TransformIn Image Compression

• Markus Gärtner
• David Havelin
• Classroom Presentation
• 1st December 2000

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Overview

• Project Goals
• Burrows Wheeler Transform (BWT)
• Application of the BWT
• Lossless Compression
• Lossy Compression
• Performance
• Conclusion

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Project Goals

• Implementation of an efficient Burrows-Wheeler
  Transform (BWT) algorithm
• Implementation of coding scheme for transformed
  data
• Analysis of lossless compression performance
• Possible combinations of BWT with Subband Coding
  schemes

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Burrows-Wheeler Transform
Lossless Reversible Block-Sorting Algorithm
Input ABDACA
1 A A B D A C
2 A B D A C A
3 A C A A B D
4 B D A C A A
5 C A A B D A
6 D A C A A B
Output CADAAB I2
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Lossless Compression
BWT
Move-to Front
Entropy
1D
Image Original MTF MTF/BWT JPEG-LS
Peppers 7.5925 6.9449 5.5727 4.513
Lena 7.4451 6.6973 5.4819 4.237
Bridge 5.7056 5.1756 4.7974 5.500
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Subband Coding
BWT MTF
Entropy
Wavelet Transform
Run- length
Q
Wavelet-BWT
1D
DCT
Q
BWT MTF
Entropy
Run- length
DCT-BWT
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Subband Coding - DCT
PSNR vs. Rate for image peppers.tif
JPEG quantization
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Subband Coding - Wavelets
PSNR vs. Rate for image peppers.tif
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Conclusions

• Image compression with BWT possible
• Limitations
• Lack of scalability
• Performance of Said-Pearlman hard to reach
• Possibilities for improvement
• Sophisticated scanning techniques (Peano)
• Run-length Encoder
• Optimized Quantization