Lecture-13 - Lecture-13 Model-base Video Compression JPEG...

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1 Lecture-13 Model-base Video Compression JPEG Baseline Coding • Divide image into blocks of size 8X8. • Level shift all 64 pixels values in each block by subtracting 2 n-1, (where 2 n is the maximum number of gray levels). • Compute 2D DCT of a block. • Quantize DCT coefficients using quantization table.
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2 JPEG Baseline Coding • Zig-zag scan the quantized DCT coefficients to form 1-D sequence. Code 1-D sequence (AC and DC) using JPEG Huffman variable length codes. JPEG BLOCK DIAGRAM DCT Q VLE VLE -1 Q -1 DCT -1 image Encoded image Decoded image
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3 JPEG Original 64K 13K 5K Difference MPEG-1 Encoder DCT - DCT Q DCT -1 Q -1 + Motion Estimat Frame Mem-1 Frame Mem-2 Motion Compenst Input Image inter intra Motion Vectors TO VLC Reconstrd Image Predicted image Prediction error Rate control ‘0’
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4 Motion Prediction c b = c b Frame k Frame k-1 b b Error - = Model-Based Image Coding
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5 Model-Based Image Coding Model-Based Image Coding • The transmitter and receiver both posses the same 3D face model and texture images. • During the session, at the transmitter the facial motion parameters: global and local, are extracted. • At the receiver the image is synthesized using estimated motion parameters. • The difference between synthesized and actual image can be transmitted as residuals.
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6 Candide Model Face Model • Candide model has 108 nodes, 184 polygons. • Candide is a generic head and shoulder model. It needs to be conformed to a particular person’s face. • Cyberware scan gives head model consisting of 460,000 polygons.
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7 Wireframe Model Fitting • Fit orthographic projection of wireframe to the frontal view of speaker using Affine transformation. – Locate three to four features in the image and the projection of a model. – Find parameters of Affine transformation using least squares fit. – Apply Affine to all vertices, and scale depth. 2 ) ( 2 2 4 2 1 a a + + = 2 1 1 1 4 3 2 1 2 2 b b y x a a a a y x
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8 Synthesis • Collapse initial wire frame onto the image to obtain a collection of triangles. • Map observed texture in the first frame into respective triangles. • Rotate and translate the initial wire frame according to global and local motion, and collapse onto the next frame. • Map texture within each triangle from first frame to the next frame by interpolation.
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9 Texture Mapping Synthesizing Realistic Facial Expressions from Photographs: Pighin et al SIGGRAPH’98
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10 3D Rigid Transformation = + = + X Y Z R X Y Z T r r r r r r r r r X Y Z T T T X Y Z 11 12 13 21 22 23 31 32 33 k i k i k k i k i k i k k i Z Y f y Z X f x = = , Wireframe coordinates Camera coordinates perspective K =camera no. 3D Rigid
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This note was uploaded on 06/12/2011 for the course COT 6505 taught by Professor Shah during the Spring '07 term at University of Central Florida.

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Lecture-13 - Lecture-13 Model-base Video Compression JPEG...

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