presentation: reviews and talks on icme 2016disp.ee.ntu.edu.tw/class/main.pdfpresentation: reviews...
TRANSCRIPT
Presentation:
Reviews and talks on ICME 2016
Cho-Ying Wu
Disp Lab
Graduate Institute of Communication Engineering National Taiwan University
December 07, 2016
Cho-Ying Wu Disp Lab 1 / 70
ICME is a high-class conference on Multimedia.
Cho-Ying Wu Disp Lab
Introduction
2 / 70
Multimedia ?
Graphics
Speech
Vision
Display
Data Mining
Video
Top conference on multimedia and related
Cho-Ying Wu Disp Lab
Introduction
3 / 70
CVPR / ICCV, ECCV, ACM MM
Generally, ICME is suitable for MS(30%), PhD(65%) students to
challenge. Recruitments from industrial are also a lot.
Topics this year : ( Video and Vision are the most popular in the trend)
Cho-Ying Wu Disp Lab
Introduction
4 / 70
Roadmap
1 Impressive Talks (1) Fei-Fei Li, Associate Professor, “A Quest for Visual Intelligence.”Computer
Science Dept. Director, Stanford Artificial Intelligence Lab.
(2) Mathias Wien, “High Efficiency Video Coding – Coding Tools and Specification: HEVC V3 and Coming Developments”
Interesting Works
(1) Gene Cheung, Xian Ming Liu “Graph Signal Processing for Image Compression
and Restoration,” Nii.
(2) Xian Ming Liu, “Random Walk Graph Laplacian based Smoothness Prior for
Soft Decoding of JPEG Images,” HIT.
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2
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Semantic Segmentation
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Fei-Fei Li
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Sparsity Model
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Mathias Wien
Gene Cheung (1) Gene Cheung, Xian Ming Liu “Graph Signal Processing for Image Compression
and Restoration,” Nii.
1
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Traditional signal property:
1. Discrete
2. Smooth
3. Band-limited frequency
Ex. DCT ( Approx. of KLT )
1
0
1cos
2
N
k n
n
X x n kN
Dictionary representation inducing sparse coding
a x
Gene Cheung
Graph Signal : Signal live on graph
Ex. Images: 2D – grid
Research interest:
1. Sampling : how to efficiently acquire signal from graph
2. Representation : Given the graph signal, how to compactly
represent it.
3. Signal Restoration : given the partial or noisy signal, how to
recover the structure.
1
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Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
D: distortion R: bit-rate
Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
Gene Cheung
Graph Laplacian :
1. Adjancency Matrix
1
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Remark: Sometimes the original problem is hard to solve, we can
change the original problem (primal problem) into dual problem
Gene Cheung
1
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Wei Hu, Gene Cheung, Antonio Ortega, Oscar Au, "Multiresolution Graph Fourier Transform for Compression of Piecewise Smooth Images," IEEE Transactions on Image Processing, vol.24, no.1, pp.419-433, January 2015.
Gene Cheung
1
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Skipped part 1. PWS image coding : Generalized GFT with intra-coding of H.264 2. Lifting implementation : lowering complexity from O(N2) to O(Nlog(N)) 3. Image denoising with sparsity and smoothness prior
Xianming Liu : Soft decoding of JPEG images
1
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Xianming Liu : Soft decoding of JPEG images
1
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Xianming Liu : Soft decoding of JPEG images
1
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Xianming Liu : Soft decoding of JPEG images
1
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Xianming Liu : Soft decoding of JPEG images
1
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Xianming Liu : Soft decoding of JPEG images
1
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Xianming Liu : Soft decoding of JPEG images
1
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Xianming Liu : Soft decoding of JPEG images
1
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Xianming Liu : Soft decoding of JPEG images
1
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Ln:Normalized Laplacian See p.56
Xianming Liu : Soft decoding of JPEG images
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Xianming Liu : Soft decoding of JPEG images
1
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Xianming Liu : Soft decoding of JPEG images
1
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Xianming Liu : Soft decoding of JPEG images
1
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Xianming Liu : Soft decoding of JPEG images
1
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Xianming Liu : Soft decoding of JPEG images
1
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