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Invariant Indexing Resistant to RST and Lossy Compression for DWT SPIHT Coded Images

Invariant Indexing Resistant to RST and Lossy Compression for DWT SPIHT Coded Images. 資工研二 陳慶鋒. Outline. Motivation Algorithm Simulation results Future work Reference. Motivation. A image may be changed by: RST lossy Compression histogram processing

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Invariant Indexing Resistant to RST and Lossy Compression for DWT SPIHT Coded Images

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  1. Invariant Indexing Resistant to RST and Lossy Compression for DWT SPIHT Coded Images 資工研二 陳慶鋒

  2. Outline • Motivation • Algorithm • Simulation results • Future work • Reference

  3. Motivation • A image may be changed by: RST lossy Compression histogram processing • We want find a invariant feature resistant to the above attacks from the image’s content

  4. DWT SPIHT Sign Map feature i c Similarity Matching DWT SPIHT Sign Map’ feature’ i’ c’ Algorithm • The flow chart

  5. Algorithm(cont.) • Get Significance Map(s) DWT SPIHT Sign Map feature i c Similarity Matching DWT SPIHT Sign Map’ feature’ i’ c’

  6. Get Significance Maps • We get the LSP of LL,LH,HL and HH band of the last level for each threshold from SPIHT bit stream,and then convert them to Significance Maps

  7. Get Significance Maps(cont.) The first threshold:32 n=num of thresholds So we get 4*n Sign maps The following threshold:16

  8. Algorithm(cont.) • Get Feature: autocorrelograms of 1s DWT SPIHT Sign Map feature i c Similarity Matching DWT SPIHT Sign Map’ feature’ i’ c’

  9. Get Feature: autocorrelograms of 1s • Choose some significance map(s) of some threshold(s), get autocorrelograms of 1s of it(them) using L1(L2) diatance

  10. Example of autocorrelograms of 1s using L1 distance • 1 sign map • 2 sign maps

  11. DWT SPIHT Sign Map feature i c Similarity Matching DWT SPIHT Sign Map’ feature’ i’ c’ Algorithm(cont.) • The similarity measure using Intersection

  12. Simulation results • Setup image: LENA compared image: LENA with rotation, scale,and translation JPEG compression FMLR BABOON PEPPER

  13. Simulation results(cont.) • Setup(cont.) image size: 512*512 DWT filter: f 9/7 DWT level: 5 the last subband size: 16*16 numbers of threshold: 5

  14. Simulation results(cont.) See the EXCEL

  15. Future work • Compare with other methods wavelet autocorrelogram EZW histogram • Add autocorrelogram of LIS • Using color images (YCbCr or RGB)

  16. Reference • Amir Said and William A. Pearlman,”A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchial Trees”,IEEE trans. On Circuits and Systems for Video Technology, vol. 6, no.3, pp. 243-250, June 1996. • [4] J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R.Zabih, “Image indexing using color correlograms,” Conf. Computer Vision and Pattern Recognit., pp.762-768,1997

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