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FRACTAL IMAGE COMPRESSION Guided By Mrs. Sheena S Presented by NithinSinkaran Roll No:57
fractal image compression Overview ,[object Object]
Why Fractal Image Compression
Mathematical Background
How does it work?
Examples
Possible Improvements,[object Object]
 Self similarity between object part and whole object
 Fractals are generated by iteratively applying transformation function to a region of space(initiator).Fractal image compression is NOT the compression of fractals ,[object Object],[object Object]
Based on college theoremLet R²(Hausdorff space) be set of two real numbers, and L be an object   Let w1,w2,w3… be some affine transforms which maps  the entire image to its subsets W(L) =U wi(L) If  distance   h(L, U wi(L) )=ɛ (a small value) Then h(L,A)= ɛ/(1-c)   where c is the contractility factor,   A is the converging abstract set Now L can be approximated to the abstractor A
fractal image compression Significance in image compression Fern created using the fractal method(fig 1) The highlighted portion of the fern is similar to the entire  Image. Application of different affine transformation on  That portion produces the entire fern(fig 2). The fern is self similar The fern creation requires only 28 numbers and can  Achieve a large amount of compression. The success of the compression depends on the amount of Self  similarity found in that image.
fractal image compression Limitation of basic theory ,[object Object]
The direct application of affine transforms to the whole set L(image)    will not always maps to its subsets due to lack of self similarities. ,[object Object],    Or  subset of L and tries to map them to self similar parts of the      Same image ,[object Object],[object Object]
fractal image compression 3.performed the following affine  transformation to each block (Di,j)=α Di,j + t0         		 where α - contrast scaling t0-luminance shift ([−255,255 ]). 4.Compare each domain block with each range block 5.Find Min Σ(Ri,j )m,n-T(Di,j))m,n 6.The transformed domain blockwhich is found to be the best approximation  for the current range block is assigned to that range block 7. The coordinates of the domain block along with value of α, t0    describing  the transformations. This is what is called the Fractal Code Book
fractal image compression Decoding Apply the transformations defined in fractal code book iteratively to some initial image Winit, until the encoded image is retrieved back. The transformation over the whole initial image can be described as follows W1 = h(Winit) W2 = h(W1) W3 = h(W2) ..... = ...... Wn = h(Wn-1) Wn  will converge to a good approximation of original image after some iterations. Greater the number of iterations greater will be the decoded similarity.
fractal image compression Quad-tree partition method ,[object Object]
Divide each parent block into 4 each blocks, or “child blocks.”
Compare each child block against a subset of all possible parent blocks.(Need to reduce the size of the parent to allow the comparison to work.) ,[object Object]
Calculate a grayscale transform to match intensity levels between large block and child block precisely.  Typically an affine transform is used (w*x = a*x + b) to match grayscale levels.,[object Object]
Compute affine transform.
Store location of parent block and child block, affine  transform components, etc .into a file(Fractal code book).
Repeat for each child block.

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Fractal Image Compression

  • 1. FRACTAL IMAGE COMPRESSION Guided By Mrs. Sheena S Presented by NithinSinkaran Roll No:57
  • 2.
  • 3. Why Fractal Image Compression
  • 5. How does it work?
  • 7.
  • 8. Self similarity between object part and whole object
  • 9.
  • 10. Based on college theoremLet R²(Hausdorff space) be set of two real numbers, and L be an object Let w1,w2,w3… be some affine transforms which maps the entire image to its subsets W(L) =U wi(L) If distance h(L, U wi(L) )=ɛ (a small value) Then h(L,A)= ɛ/(1-c) where c is the contractility factor, A is the converging abstract set Now L can be approximated to the abstractor A
  • 11. fractal image compression Significance in image compression Fern created using the fractal method(fig 1) The highlighted portion of the fern is similar to the entire Image. Application of different affine transformation on That portion produces the entire fern(fig 2). The fern is self similar The fern creation requires only 28 numbers and can Achieve a large amount of compression. The success of the compression depends on the amount of Self similarity found in that image.
  • 12.
  • 13.
  • 14. fractal image compression 3.performed the following affine transformation to each block (Di,j)=α Di,j + t0 where α - contrast scaling t0-luminance shift ([−255,255 ]). 4.Compare each domain block with each range block 5.Find Min Σ(Ri,j )m,n-T(Di,j))m,n 6.The transformed domain blockwhich is found to be the best approximation for the current range block is assigned to that range block 7. The coordinates of the domain block along with value of α, t0 describing the transformations. This is what is called the Fractal Code Book
  • 15. fractal image compression Decoding Apply the transformations defined in fractal code book iteratively to some initial image Winit, until the encoded image is retrieved back. The transformation over the whole initial image can be described as follows W1 = h(Winit) W2 = h(W1) W3 = h(W2) ..... = ...... Wn = h(Wn-1) Wn will converge to a good approximation of original image after some iterations. Greater the number of iterations greater will be the decoded similarity.
  • 16.
  • 17. Divide each parent block into 4 each blocks, or “child blocks.”
  • 18.
  • 19.
  • 21. Store location of parent block and child block, affine transform components, etc .into a file(Fractal code book).
  • 22. Repeat for each child block.
  • 23. Lots of comparisons andcalculations.For 256x256 original image and 16x16 sized parent blocks 241*241 = 58,081 block comparisons.
  • 24.
  • 25.
  • 26.
  • 27. Each range and domain blocks are further divided in to 4 parts
  • 28. Average intensities are calculated for each block
  • 29. Based on the average intensities it falls into any one of the 3 major classes
  • 30. Comparison is done with blocks belonging to similar class only.
  • 32. Nearest neighbour search scheme(D. Saupe and U. Freiburg)
  • 33. fractal image compression is equivalent to the multidimensional nearest neighbour search.
  • 34. optimal domain-range pairs is equivalent to solving nearest neighbour problems in a suitable Euclidean space
  • 35. Multi-dimensional nearest neighbor searching operates in logarithmic time
  • 36.
  • 37. Behaves almost like a fractal image
  • 38. It can be zoomed at any magnitude without producing the jagged effect
  • 39.
  • 40. fractal image compression EXAMPLES Image Details (JPEG) Original Size(KB) Compressed Size(KB) Lena 256X256(24bit) 84.3 17.5 Brick 256X256(24bit) 66.2 9.91 45.9 5.91 Leaf 256X256(24bit)
  • 41.
  • 42.
  • 43.
  • 44. Used to create .FIF files from JPEG,PNG etc.. Fractal Imager showing 8:1 zooming of a Leaf. Original image(left), .FIF image (right)
  • 45.
  • 46. Used as a plug-in to software like Adobe Photoshop, Adobe Light room
  • 47. Images can be enlarged up to 1000 times its original sizeGenuine fractals in Adobe Photoshop CS5

Notes de l'éditeur

  1. Here we used similar sized blocks. This reduces the efficiency. For better compression the size of blocks should be non uniform . Hence we use quad tree partitioning
  2. The compression can be further improved by multiple division of child blocks(Quad partition), but increases the number of iterations And comparisons