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Image Compression: Comparative Analysis of Basic Algorithms

Image Compression: Comparative Analysis of Basic Algorithms. Yevgeniya Sulema (Ukraine) Samira Ebrahimi Kahou (Iran) National Technical University of Ukraine “Kyiv Polytechnic Institute” sulema@scs.ntu-kpi.kiev.ua samira_ebrahimi@hotmail.com. Outline.

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Image Compression: Comparative Analysis of Basic Algorithms

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  1. Image Compression: Comparative Analysis of Basic Algorithms Yevgeniya Sulema (Ukraine) Samira Ebrahimi Kahou (Iran) National Technical University of Ukraine “Kyiv Polytechnic Institute” sulema@scs.ntu-kpi.kiev.ua samira_ebrahimi@hotmail.com

  2. Outline Existing compression methods and classification Criteria How to choose image set for testing Realizing algorithms Getting numerical values on chosen criteria Verifying results obtained from test Analysis and conclusion

  3. Compression algorithms-Classification 5 Main Classification Types chosen. By data type : General algorithms Algorithms for audio-compression Algorithms for image-compression Algorithms for video-compression

  4. Compression algorithms-Classification (..2) By data source : Dynamic Static By redundancy type : Statistical redundancy reduction Spatial redundancy reduction

  5. Compression algorithms-Classification (..3) By restoring the original dataset: Lossless Lossy By computational approach : Statistical Dictionary Transformation based Hybrid

  6. Classes of Images Business graphics (schemes, diagrams, charts) Pictures created in graphic editors (photoshop) Photorealistic images (photos, textures) Coefficient of correlation can be used between an analyzed (test) image and an etalon image to classify images :

  7. Sample images Image with two monochrome areas Image with large monochrome fields Gradient image Image with small monochrome fields

  8. Correlation coefficients(Sample images)

  9. Criteria Compression ratio Time of compression Time of decompression Peak signal-to-noise ratio (PSNR)MSE : Mean Squared Error Coefficient of correlation between original and decompressed image

  10. Matlab image processing Toolbox

  11. Why Matlab? It provides a comprehensive set of reference-standard algorithms. The software is a collection of functions that extend the capability of the MATLAB. The toolbox supports a wide range of image processing operations. Most toolbox functions are written in the open MATLAB language, giving us the ability to inspect the algorithms, modify the source code.

  12. Algorithms: Lossless : LZW LZ77 Huffman Adaptive Huffman Shannon-Fano Arithmetic Lossy : • JPEG(Coarse and Fine) • Wavelet(Daubechies, Coiflets, Symlets, Discrete Meyer wavelet, Biorthogonal, Reverse Biorthogonal) • SPIHT • Fractal

  13. LosslessTime of compression

  14. LosslessTime of decompression

  15. LosslessCompression ratio

  16. Lossless Algorithm Observation Dictionary Based Algorithms most Effective LZ77 – prime example from our research Minimal Time for Compression Minimal Time for Decompression High Compression Ratio

  17. LossyTime of compression

  18. LossyTime of decompression

  19. LossyCompression ratio

  20. LossyCorrelation coefficient

  21. LossyPSNR

  22. Lossy Algorithm Observations Fractal Algorithm not practical. All remaining algorithms are Hybrid Combination of procedures can result in increased quality.

  23. Conclusion Our research allows us to draw 3 main conclusions: The selection of the proper compression algorithm for each image class should be made Hybrid algorithms, JPEG, can be modified in order to achieve better result Combination of a dictionary and transforms most promising.

  24. Thank YOU! Questions…???samira_ebrahimi@hotmail.com

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