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1/20 Document Segmentation for Image Compression 27/10/2005 Emma Jonasson Supervisor: Dr. Peter Tischer
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2/20 The Big Picture Background Aims Suggested Solution Tests and Results Conclusion Future Work Overview
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3/20 The Big Picture Find structure in data Data = binary images of documents Documents contain: – Text – Pictures – Diagrams – Etc.
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4/20 Why structure in data? Content-Based Image Retrieval (CBIR) –E.g. find documents with photos Optical Character Recognition (OCR) –Extract text from documents JBIG 2 (Joint Bi-level Image experts Group) and MRC (Mixed Raster Content) –Use of structure can improve compression
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5/20 What’s out there? JBIG 2 –Encoder tells decoder what the segmentation is –No standard segmentation algorithm DjVu –Intended for colour images –Different layers = different segments
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6/20 Discover structure in binary images, to: – Enhance compression – Explore the influence of different kinds of segmentation – Explore novel approach to segmentation pre-processing Project Aims
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7/20 Solution Claim: –Segments are groups of pixels which have similar information content Approach: –Extract information content from image and group according to similarity of content
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8/20 Pipeline of Solution Whitening transformation BlockingSegmentation Compression
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9/20 Whitening transformation Operates on “contexts” Reversible Black pixels = Incorrect predictions White pixels = Correct predictions
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10/20 Blocking Group pixels into n x n blocks Rough estimate of information content Density of black pixels in transformed image indicates different information content.
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11/20 Segmentation of blocked, whitened image Segmentation techniques: – Manual By hand – Automatic Thresholding Clustering Outcome: – Segment map
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12/20 Segmentation-Based Coding Encoding of segment information – Compress current pixel based on neighbouring pixels Encoding of binary image given segmentation – Compress current pixel based on neighbouring pixels and type of segment
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13/20 Segmentation Issues Trade-off between: –Information needed to encode segmentation –Information needed to encode image given segmentation I.e. how accurate should the segmentation be?
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14/20 Evaluation of Segmentation Subjective measure –Is segmentation optimal from a human being’s point of view? Objective measure –Code length of compressed image
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15/20 Testing Seven “representative” test images Various whitening contexts Various block sizes Different segmentation techniques Different segmentation granularity
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16/20 Results No single “best” whitening context for all images –Unwhitened generally worse Optimal block size is image-dependent –8 x 8 and 16 x 16 generally perform well No single “best” granularity for all images
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17/20 Results Compression ratio
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18/20 Conclusion No single optimal segmentation Better than JBIG 1 Worse than JBIG 2 –Only compared with 1 image due to patent restrictions –Better textual compression needed to compete with JBIG 2
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19/20 Future Work Test other segmentation techniques Semi-automatic segmentation MML clustering Implement “connected black regions”- compression used in JBIG 2 Represent segmentation in a different format
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20/20 Any Questions?
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