wavelet based embedded coding for images

8/8/2019 Wavelet Based Embedded Coding for Images

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Wavelet Based Embedded Coding for Images(A novel extension of SPIHT and SPECK)

by

Abhishek Kr Singh

(07ET6001)

Under the guidance of

Prof. A. K. Ray (CET)

Dr. S. Mahapatra (ECE)


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Agenda/Topics to Be Covered

Objective

Why wavelet based coding

Why SPIHT

SPIHT and arithmetic coding

Context model

SPIHT and resolution scalability

Conclusion

References


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Objective of the project

Modifying SPIHT algorithm to improve the

PSNR performance and facilitate resolutionscalable decoding.


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Wish List for the proposed scheme

Better PSNR performance

Rate scalability

Resolution scalability

Fast, simple encoding and decoding.


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Motivation for the present work

Presence of blocking artifacts at low bit encoded JPEG images

Block splitting

DCT Quantization

Complex structure of JPEG2000 coding schemes

R D optimization step

Fractional Bit plane coding


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JPEG coding Scheme

Block-splitting of image

DCT

Quantization

Entropy-coding

Blocking artifacts appears atmedium to low bit rate encoding


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Blocking artifacts in DCT based coding


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Transition towards Wavelet based coding schemes

Whole image is transformed withoutsplitting into artificial blocks of smallsize

Blocking artifacts are completelyremoved

Better compression results

Fast implementation by sub banddecomposition method

Excellent methods to code DWTcoefficients


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Sub band decomposition of image


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Bit plane coding of DWT coefficients

More significant bits are coded first

Coding process is progressive in nature

Bit stream generated is embedded in nature

SNR scalable bit stream Fine bit rate scalability

But the compression ratio can be

improved further if..?


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significance and refinement information


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significance and refinement information

Still enough redundancy in the significanceinformation

Redundancy in significance information can be

exploited within subband and across subband No redundancy in refinement information

Separately coding significance and refinementinformation


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Exploiting redundancy in thesignificance information

Tree based approach (across the band) EZW (Zero tree)

SPIHT (Spatial orientation tree)

-- performs well for natural images

Block Based approach (within a subband)

SPECK

EBCOT

--performs well even for images with high details


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Tree based approach (hypothesis)


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All the schemes code a bit plane in two passes

Significance and subordinate pass (in EZW)

Sorting and Refinement pass (in SPIHT)

Redundancy in the insignificance information acrossthe subband is exploited

Use of different data structure to group the

insignificant information Zero tree (in EZW)

LIS (in SPIHT)

Tree based approach(similarities and differences )


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Why SPIHT .. ?

Uses a data structure (LIS) which is more powerfulthan Zero tree in grouping the insignificanceinformation

LIS works at many places where Zero tree conceptfails to group the insignificance information.

The coder is simple and fast

--- thus we explore SPIHT


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Exploring SPIHT

Effect of decomposition level on PSNR

PSNR performance of SPIHT at differentbit rate

Plots

Picture decoded at different bit rate SPIHT and Arithmetic coder

PSNR gain at different bit rate (plot)


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Number of decomposition level in SPIHT


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Number of decomposition level and PSNR


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PSNR at different bit rate in SPIHT


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Lena image decoded at 0.1 bpp PSNR: 26.72dB


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Lena image decoded at 0.25 bpp PSNR: 32.14dB


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Lena image decoded at 0. 5 bpp PSNR: 35.95dB


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Lena image decoded at 1 bpp PSNR: 39.84dB


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Lena image decoded at 2 bpp PSNR: 44.90 dB


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Original Lena image


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SPIHT with Arithmetic Coding


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Finding alternative for arithmetic coder

Where does the redundancy lies..?

Recall the separate coding of significance andrefinement information

All the redundancy in significance information No redundancy in refinement bits

thus we should investigate the bitscorresponding to the coded significance

information.


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Significance information generation in SPIHT

LIP processing

LIS processing

remaining redundancy is generated while LIS

processing. Why?

correlation in the significance information of the

offspring.

different context shows some trend


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Probability distribution of different contexts


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Test image of Pami


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Test image of Pami


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Huffman code for different contexts

Probability distribution is almost same

Average of these probability distributions isused to find the Huffman code for each context

These codes are used to represent the contextsin the output bit stream

the PSNR gain obtained using these

Huffman codes is about 0.2-0.3 dB

No loss in the speed of the encoder

PSNR performance of Fixed context model with SPIHT


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PSNR performance of Fixed context model with SPIHT

PSNR performance: Context model vs Arithmetic coder


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PSNR performance: Context model vs Arithmetic coder


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Resolution scalability and SPIHT


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In each bit plane

Significance pass for resolution level 1

Refinement pass for resolution level 1

Significance pass for resolution level 2

Refinement pass for resolution level 2



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Decoded image at resolution [128,128] bit rate:0.1


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Decoded image at resolution [128,128] bit rate:0. 5


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Decoded image at resolution [128,128] bit rate: 1


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Decoded image at resolution [256,256] bit rate: 0.1


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Decoded image at resolution [256,256] bit rate: 1


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R f


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References

A. Islam and W. A. Pearlman, An Embedded and Efficient Low- Complexity

Hierarchical Image Coder, Visual Communication and Image processing '99,Proceeding of SPIE Vol. 3653, pp. 294-305, Jan. 1999.

Khalid Sayood. Introduction to Data Compression,3/e.Morgan KaufmannPublishers,2006.

J. M. Shapiro, "Embedded image coding using zero trees of wavelet coefficients," IEEETrans. on Signal Processing, vol. 41, 1993, pp. 3445-3462.

A. Said and W.A. Pearlman, "A New Fast and Efficient Image Codec Based on SetPartitioning in Hierarchical Trees, "IEEE Trans. on Circuits and Systems for Video

Technology, vol. 6., pp. 243--250, June 1996.

D. Taubman, High performance scalable image compression with EBCOT, IEEETrans. Image Proc., vol. 9, pp. 11581170, July 2000.

D.S.Taubman and M. W. Marcellin. JPEG2000: Image Compression Fundamentals,Standards, and Practice. Boston: Kluwer,2002

C. Chrysafis, A. Said, A. Drukarev, A. Islam, and W. A. Pearlman, "SBHP- A LowComplexity Wavelet Coder," IEEE Int. Conf. on Acoustics, Speech and Signal

Processing (ICASSP 2000), Istanbul, Turkey, June 5-9, 2000.


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Thank You

wavelet based embedded coding for images

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