This book illustrates utilization of fast transform, sparse representation and low rank analysis as tool in multidimensional signal processing and focuses on discrete cosine transform, optimization of double tree wavelet transform in coding and noise reduction, self-return compression perception of image signal. With orignal research results, the book is an essential reference for electrical engineering researchers and engineers.
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 imag
e/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 signa
l 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
Qionghai Dai, Tsinghua University, Beijing, China
