1 / 21

Efficient Fractal Image Coding Using Side Match Vector Quantizers

This paper discusses the efficient coding of images using side match vector quantizers (SMVQ) and gradient match vector quantizers (GMVQ). It compares the non-iterative fractal block coding (FBC) with traditional vector quantization (VQ) techniques. The FBC method records the fractal code rather than codeword indexes, leading to a more complex encoding process. The non-iterative FBC approach involves domain and range block partitioning, domain pool generation, and efficient encoding algorithms. The study presents experimental results showing that GMFVQ and SMFVQ techniques offer improved image coding efficiency, reducing bit rates by 10%-20% compared to non-iterative FBC. The reconstructed images exhibit high quality with minimal blocking artifacts. However, building large super codebooks and organizing state codebooks require additional computational resources.

orra
Download Presentation

Efficient Fractal Image Coding Using Side Match Vector Quantizers

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Gradient Match and Side Match Fractal Vector Quantizers for Images Source: IEEE Transaction on Image Processing, Vol. 11, No. 1, pp. 1- 9, January. 2002 Authors: Hsuan T. Chang Speaker: Chia-Lin Kao Date: 2004/04/26

  2. Outline • FBC versus VQ • Non-iterative Fractal Image Coding • SMVQ/GMVQ • Image Partition • Experimental results • Conclusions

  3. FBC versus VQ • VQ records the indexes of the codewords and FBC records the fractal code. • VQ’s codebook is predesigned and FBC’s domain blocks are extracted from the image itself. • Computational load of the FBC encoding and decoding processes is much higher the that of VQ.

  4. Non-iterative fractal block coding Generate the efficient domain pool Output the header and the fractal code image F Partition into non-overlapping range block If thevariance of the range block <= THv Code by the mean of the range block Else Find a domain block which similar to the range block

  5. Mean value: B=8 B Non-iterative fractal block coding (Generate the domain pool) image F I range block Rr I=512 I/B=64 domain block Dd (B x B) I/B Domain pool: a set of all domain blocks. mean image M

  6. Mean value: B=8 B Variance: Non-iterative fractal block coding (Encode the fractal code) I range block Rr I=512 image F Header information: 0 If thevariance of the range block <= THv Save the mean of Rras a fractal code

  7. Non-iterative fractal block coding (Encode the fractal code) Header information: 1 If thevariance of the range block > THv Find a domain block which similar to the range block The new affine transformation (新相仿轉換) ι:isometry transform D: all pixel values in a domain block μD: the mean of D μR: the mean of the correlative range block α : the contrast scaling; α ={n/4, n=1,2,3,…,8}

  8. Non-iterative fractal block coding (Encode the fractal code) ι: isometric transformation A fractal code contains: the isometric transformation(3b) ; the contrast scaling(3b) ;mean of the range block(6b) ; the position of domain block(12b)

  9. Non-iterative fractal block coding (Encode the fractal code) • The fractal code: (output) • Header information: 0010101…1 • Data information: 001011 010001000 011 100000 000000110101… mean=44 mean=68 ι :(a) α =4 mean=128 53th block

  10. Non-iterative fractal block coding (Generate the efficient domain pool) Ex:8-4+1=5 • We have (I/B-B+1)*(I/B-B+1)+1 domain blocks. (I:512; B:8) • (512/8-8+1)*(512/8-8+1)+1=(64-8+1)*(64-8+1)=3250 • Some neighboring domain blocks are similar to each other.

  11. Example: ND=225 Non-iterative fractal block coding (Generate the efficient domain pool) • Block-averaging method T: the sampling period ND: the number of domain blocks

  12. Non-iterative fractal block coding (Generate the efficient domain pool)

  13. SMVQ/GMVQ

  14. Super codebook and State codebooks • Super codebook =ι* α* Domain pool • State codebook(SC) is a subset of the super codebook. • State codebook contains N codewords sorting by the gradient match error Egm or the smallest side match error Esm.

  15. Image Partition • 4x4 • 8x8 • 8x8(parent) and 4x4(child) range blocks

  16. Ih: header, Iι: isometry type, Iμx:mean value, Iα:contrast scaling factor, IPD: domain block’s position, ISC:state codebook’ position

  17. Experimental result

  18. Non-iterative FBC block size 4x4 (Vth=25)

  19. Non-iterative FBC block size 8x8 (Vth=25)

  20. GMFVQ SMFVQ Experimental result

  21. Conclusions • Propose GMFVQ and SMFVQ for image coding saving about 10%-20% bit rate for the noniterative FBC technique. • Reconstructed image have the excellent quality with negligible blocking effects at edges. • Needing extra computations in constructing large super codebook and sorting the codewords to obtain state codebooks from the super codebook.

More Related