1. Binary Image Compression Using Efficient Partitioning into Rectangular Regions IEEE Transactions on Communications Sherif A.Mohamed and Moustafa M. Fahmy (IEEE Fellow) A New Compression Technique for Binary Text Images IEEE Symposium on Computers and Communications (ISCC) Azhar Quddus and Moustafa M. Fahmy (IEEE Fellow)

2. Binary Image Compression Using Efficient Partitioning into Rectangular Regions IEEE Transactions on Communications Sherif A.Mohamed and Moustafa M. Fahmy (IEEE Fellow)

3. Outline Introduction Image Partition into Rectangles Algorithm for Partitioning Coordinate Data Compression Test Images of different types and sizes Experimental Results Conclusion

4. Introduction This paper propose a new binary image coding scheme that can achieve excellent compression results via –partitioning the black regions of the input image into nonoverlapping rectangular regions –efficiently encoding the locations of the vertices of these rectangles.

5. Image Partition into Rectangles Binary Image Encoding Matrix R using the proposed approach

6. Algorithms for Partitioning Partition the original image into the minimum number of nonoverlapping rectangles. The Optimal Rectangular P artitioning (ORP) algorithm is very slow and has very high complexity. A simple and fast Near-optimal Rectangular Partitioning (NRP) algorithm proceeds as follows:

7. Algorithms for Partitioning (Cont.) 1. During the raster (left to right and top to bottom) scan process, when an unprocessed black pixel is encountered, it is considered as the top left vertex of a rectangle. The same location in the matrix R should be set to 1 to indicate the existence of a top left vertex of a new rectangle. 2. All the unprocessed black pixels to the right of the above pixel are included in the new developing rectangle until a terminating, white or processed black, pixel is encountered. The pixel exactly to the left of the terminating pixel now represents the top right vertex of the new rectangle. 3. The rectangle then extends downward by including all the unprocessed black pixels bounded by the column locations of the top left and top right vertices. The downward extension stops if the following. a) One, or more, white pixel exists in the new encountered row within the vertical locations of the left and right vertices of the rectangle. b) The new downward row has unprocessed black pixels exactly to the left and exactly to the right of the left and right vertices, respectively.

8. Algorithms for Partitioning (Cont.) 4. The rightmost pixel in the last row of the developing rectangle is considered as the bottom right vertex of the rectangle. Hence, its location in the matrix R should be set to 2. If the location of the bottom rights vertex is the same as the that of the top left one, then this location in the matrix R should be changed to -1 to indicate the case of a one pixel rectangle. 5. All the pixels in the above rectangle should be indicated as processed black pixels in order not to be processed again. This can be achieved by changing their values in the matrix A to 0 so that they are not to be processed any further. 6. The search of another rectangle resumes from the pixel next to the one identified as the top left vertex of the last encountered rectangle until the whole image is scanned.

10. Coordinate Data Compression Matrix REncoding Bits 11010 0 10011101 01011011001 10000 001010 011000 1001010000

11. Test Images of different types and sizes (a) 256x256 (b) 512x512 (c) 379x374

12. Test Images of different types and sizes (f) 767x572 (d) 346x508 (e) 629x576

13. Experimental Results

14. Experimental Results (Cont.)

15. Conclusion This paper propose a new binary image coding scheme that can achieve excellent compression results via –partitioning the black regions of the input image into nonoverlapping rectangular regions –efficiently encoding the locations of the vertices of these rectangles. The performance of NRP is indeed close to optimal (ORP) while being simpler to implement.

16. A New Compression Technique for Binary Text Images IEEE Symposium on Computers and Communications (ISCC) Azhar Quddus and Moustafa M. Fahmy (IEEE Fellow)

17. Outline Introduction Image Partition into Overlapping Rectangles Algorithm for Partitioning Coordinate Data Compression Test Images Experimental Result Conclusion

18. Introduction This paper propose a new binary image coding scheme that can achieve excellent compression results via –partitioning the black regions of the input image into overlapping rectangular regions –efficiently encoding the locations of the vertices of these rectangles. The complexity is justified by the gain obtained in the compression ratio. –Less the numbers of rectangles than nonoverlapping partitioning –More complex than nonoverlapping partitioning

19. Image Partition into Overlapping Rectangles Nonoverlapping Partitioning Overlapping Partitioning

20. Algorithms for Partitioning Partition the original image into the minimum number of overlapping rectangles. Problem: –Partial Overlapping –Conflict

21. Problem: Partial Overlapping Situation1:

22. Problem: Partial Overlapping Situation2:

23. Problem: Conflict Partitioning of Image1 Identical Matrix RPartitioning of Image2

24. Problem: Conflict Conflicting Image after Splitting Matrix R

25. Coordinate Data Compression Matrix REncoding Bits 11010 0 10011101 01011011001 10000 001010 011000 1001010000

26. Test Images Hindi-English Text (64x64)Arabic-English Text (64x64)

27. Experimental Result

28. Conclusion This paper propose a new binary image coding scheme that can achieve excellent compression results via –partitioning the black regions of the input image into overlapping rectangular regions –efficiently encoding the locations of the vertices of these rectangles. The complexity is justified by the gain obtained in the compression ratio. –Less the numbers of rectangles than nonoverlapping partitioning –More complex than nonoverlapping partitioning