Qionghai Dai, Tsinghua University, Beijing, China

• Table of Content:Chapter 1 Review on multidimensional signal processing1.1 Introduction1.2 Multi-dimensional signal: fast transformation1.3 Multi-dimensional signal: sparse representation1.4 Multi-dimensional signal: low rank analysis1.5 SummaryChapter 2 Multi-dimensional discrete cosine transform matrix and fast decomposition2.1 Introduction2.2 Dct transformation and matrix decomposition2.3 M ddct and m d ratio2.4 M d ratio dct fast algorithm2.5 Computational complexity comparison2.6 SummaryChapter 3 Multidimensional discrete wavelet transform: vlsi architecture3.1 Introduction3.2 Multi-dimensional dwt transformation framework3.3 Comparison and evaluation3.4 SummaryChapter 4 Multi-dimensional signal: sparse representation theory and application4.1 Introduction4.2 Compression perception4.3 Application of compression perception4.4 SummaryChapter 5 Discrete wavelet transform based on image/video coding5.1 Introduction5.2 Dual-tree discrete wavelet transform5.3 Image coding based on ddwt5.4 Adaptive DWTWT5.5 Image/Video Encoding Based on addwp5.6 SummaryChapter 6 Low-order analysis of multi-dimensional signal: theory and application6.1 Introduction6.2 Matrix rank6.3 Matrix low rank sparse decomposition6.4 Applications and examples6.5 SummaryChapter 7 Sparse structure visual information perception7.1 Introduction7.2 Logarithms and heuristic perception algorithm7.3 Log sum approximation in the data analysis application7.4 Log sum approximation in stereo reconstruction application7.5 Summary7.6 AppendixChapter 8 Dynamic reconstruction of the dynamic topography8.1 Introduction8.2 Research updates8.3 3D reconstruction of dynamic scene based on 3D motion estimation8.4 Experimental results and analysis8.5 SummaryChapter 9 Low-rank decomposition of multidimensional signal and adaptive reconfiguration9.1 Foreword9.2 Low-rank accumulate matrix construction and low-rank decomposition of multidimensional signal 9.3 Applications of low rank decomposition in compressive perception image reconfiguration9.4 Application of low rank decomposition in super resolution9.5 SummaryReferences