Segmentation-Based Image Compression 以影像切割為基礎的影像壓縮技術 Speaker: Jiun-De Huang Advisor: Jian-Jiun Ding Graduate Institute of Communication Engineering National Taiwan University, Taipei, Taiwan, ROC

2 Outline Introduction to Image Compression Segmentation-Based Image Compression Edge Detection Image Segmentation Boundary Description and Compression Proposed Methods for Boundary Description Internal Texture Compression Comclution Future Work

3 Introduction to Image Compression Why we need to compress the image? –Save disk space –Save transformation bandwidth The common type of image compression –DCT-based method: JPEG –Wavelet-based method: JPEG2000

4 Introduction to Image Compression Color Component of an Image Transform Coding ( DCT or Wavelet ) Quantization Entropy Coding Bit-stream Image compression model Bit-stream Transform Decoding Entropy Decoding Color Component of an image Encoder Decoder

5 Segmentation-Based Image Compression Image segments of DCT: Object-oriented segments:

6 Segmentation-Based Image Compression Segmentation-based image compression model Arbitrary-Shaped Transform Coding Quantization & Entropy Coding Bit-stream Image Segmentation Boundary Transform Coding Quantization & Entropy Coding Internal texure Boundary Coefficients of transform bases Boundary descriptor An image

7 Segmentation-Based Image Compression Advantage –Pixels in the same segment have extremly high correlation, the compression ratio can be higher. – The boundary of a segment is recorded separately, it may make the image clear in high compression ratio. –Application in image recognize Disadvantage –Large time to encode and decode –Hard to find a common way to segment various images.

8 Edge Detection First-order derivatives Second-order derivatives Hilbert transform Short time Hilbert transform

9 Edge Detection Using differentiationUsing HLT Sharp edge Step edge With noise Ramp edge

10 Edge Detection Short Time Hilbert Transform –Impulse responses and their FTs of the SRHLT for different b. We can compare them with the impulse response of the differential operation and the original HLT (a) (b) Time domain Frequency domain Hilbert transform FT (i) (j) differentiation FT

11 Edge Detection Short Time Hilbert Transform –Using SRHLTs to detect the sharp edges, the step edges with noise, and the ramp edges. Here we choose b = 1, 4, 12, and 30.

12 Edge Detection (a) Original image (b) Results of differentiation (c) Results of the HLT (d) Results of the SRHLT, b=8 (a) image+noise, SNR=32 (b) Results of differentiation Short Time Hilbert Transform

13 Image Segmentation Thresholding Gray-level histograms that can be partitioned by (a) Single threshold, and (b) multiple thresholds

14 Image Segmentation Edge Linking –Hough transform Two point in the coordinate The coefficient space

15 Image Segmentation Edge Linking –Hough transform Two points in the Polar coordinate Coefficient space

16 Image Segmentation Region Growing Region Splitting and Merging

17 Image Segmentation Watershed

18 Boundary Description and Compression Polygonal approximations –Merging techniques –Splitting techniques

19 Boundary Description and Compression Fourier descriptor –Set the coordinate of the K-point boundary as a series of complex number s(k), k=0,1,…,K-1. –The Fourier descriptor is define as the DFT of s(k). The DFT of s(k) The inverse DFT of a(u)

20 Boundary Description and Compression Fourier descriptor –If we only use the first P coefficients, the detail of the recover boundary will be lost. Smaller P becomes, more detail lost. Original image R=30%R=20%R=10% Compression rate: R = P/K

21 Proposed Methods for Boundary Description Improvement of Fourier descriptor –We segment the boundary with the corner point and only compute the Fourier desriptor of the boundary segment –However, if we do not use the whole coefficients, the recovery boundary segment will be closed due to the discontinuous of the two end point u a(u)a(u) 0 P K Boundary segment Fourier descriptor Recover boundary truncate

22 Proposed Methods for Boundary Description Improvement of Fourier descriptor –To solve the non-closed problem, we adapt the following steps: 1.Record the coordinate of the two end of the boundary segment and shift them to the original of coordinate 2.Shift the other boundary points linearly according to its distance with the end point 3.Add a new boundary which is odd symmetry to the original one Boundary segment Shift linearly Add a new boundary

23 Proposed Methods for Boundary Description Improvement of Fourier descriptor 4.Compute the Fourier descriptor to the new boundary which is closed and is continuous in the two end points 5.Because the new boundary is odd symmetry, the Fourier descriptor is odd symmetry, too. There is, we only need to record the first K points of the Fourier descriptor. u a(u)a(u) 0 K 2K-2 Fourier descriptor useless

24 Proposed Methods for Boundary Description Improvement of Fourier descriptor –Simulation R=20%R=10% R= 7% Original image general Fourier descriptor modified Fourier descriptor

25 Internal Texture Compression v u The 8x8 DCT basis

26 Internal Texture Compression v u The Arbitraryly-shaped DCT basis

27 Internal Texture Compression v u The Arbitraryly-shaped DCT basis Use zig-zag order to do Gram-Schmidt orthonormalize

28 Internal Texture Compression The Arbitraryly-shaped DCT orthnormal basis

29 Internal Texture Compression An arbitraryly-shaped image The 37 AS-DCT coefficients AS-DCT Example:

30 Conclusion The compression rate depend on the complex of the image content. This compression method is better when the image content is simple. There are various method in each step, they suit different image respectly.

31 Future Work Find a better method of segmentation which is suit to this compression method. Automatic analysis the property of the image and choose the fittest method in each step. How to apply this compression method to the image recognize technique.