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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. Outline. Introduction to Image Compression Segmentation-Based Image Compression
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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
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
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
Introduction to Image Compression • Image compression model Encoder Transform Coding ( DCT or Wavelet ) EntropyCoding Quantization Bit-stream Color Component of an Image Decoder EntropyDecoding Transform Decoding Color Component of an image Bit-stream
Segmentation-Based Image Compression Image segments of DCT: Object-oriented segments:
Segmentation-Based Image Compression • Segmentation-based image compression model Boundary Boundary descriptor Boundary Transform Coding Quantization & Entropy Coding An image Image Segmentation Bit-stream Arbitrary-Shaped Transform Coding Quantization & Entropy Coding Internal texure Coefficients of transform bases
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.
Edge Detection • First-order derivatives • Second-order derivatives • Hilbert transform • Short time Hilbert transform
Edge Detection Using differentiation Using HLT Sharp edge Step edge With noise Ramp edge
Frequency domain Time domain Frequency domain Time domain (c) (a) (d) (b) 1 1 1 1 SRHLT, b=0.25 Hilbert transform FT 0 FT 0 0 0 -1 -1 -1 -1 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 (e) (f) 1 1 SRHLT, b=1 (j) (i) 0 FT 0 1 10 differentiation -1 -1 FT 0 0 -2 -1 0 1 2 -2 -1 0 1 2 (g) (h) 1 -1 -10 1 SRHLT, b=4 -2 -1 0 1 2 -2 -1 0 1 2 FT 0 0 -1 -1 -2 -1 0 1 2 -2 -1 0 1 2 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.
b = 4 b = 12 b = 1 b = 30 (a) (h) (g) (b) 1 1 0.5 1 0 0 0 0 -1 -1 -0.5 -1 (c) (d) 0 50 100 0 50 100 100 0 50 100 (i) (j) 1 1 1 0.5 0 0 0 0 -1 -1 -0.5 -1 0 50 100 (e) 0 50 100 (k) 0 50 100 (f) (l) 0 50 100 1 1 1 0.5 0 0 0 0 -1 -1 -0.5 -1 0 50 100 0 50 100 0 50 100 0 50 100 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.
Edge Detection • Short Time Hilbert Transform (a) Original image (b) Results of differentiation (a) image+noise, SNR=32 (b) Results of differentiation
Image Segmentation • Thresholding Gray-level histograms that can be partitioned by (a) Single threshold, and (b) multiple thresholds
Image Segmentation • Edge Linking • Hough transform The coefficient space Two point in the coordinate
Image Segmentation • Edge Linking • Hough transform Two points in the Polar coordinate Coefficient space
Image Segmentation • Region Growing • Region Splitting and Merging
Image Segmentation • Watershed
Boundary Description and Compression • Polygonal approximations • Merging techniques • Splitting techniques
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)
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. Compression rate: R = P/K R=30% R=20% R=10% Original image
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 a(u) truncate u 0 PK Recover boundary Boundary segment Fourier descriptor
Proposed Methods for Boundary Description • Improvement of Fourier descriptor • To solve the non-closed problem, we adapt the following steps: • Record the coordinate of the two end of the boundary segment and shift them to the original of coordinate • Shift the other boundary points linearly according to its distance with the end point • Add a new boundary which is odd symmetry to the original one Boundary segment Shift linearly Add a new boundary
Proposed Methods for Boundary Description • Improvement of Fourier descriptor • Compute the Fourier descriptor to the new boundary which is closed and is continuous in the two end points • 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. a(u) useless u 0 K 2K-2 Fourier descriptor
Proposed Methods for Boundary Description • Improvement of Fourier descriptor • Simulation Original image R= 7% R=20% R=10% general Fourier descriptor modified Fourier descriptor
Internal Texture Compression u The 8x8 DCT basis 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 v
Internal Texture Compression u The Arbitraryly-shaped DCT basis 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 v
Internal Texture Compression u The Arbitraryly-shaped DCT basis 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 Use zig-zag order to do Gram-Schmidt orthonormalize v
Internal Texture Compression The Arbitraryly-shaped DCT orthnormal basis 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
Internal Texture Compression Example: An arbitraryly-shaped image The 37 AS-DCT coefficients AS-DCT
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.
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.