Estimating the Jacobian of the Singular Value Decomposition: Theory and Applications
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Learning Matrix Space Image Representations
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
SVD-Based Approach to Transparent Embedding Data into Digital Images
MMM-ACNS '01 Proceedings of the International Workshop on Information Assurance in Computer Networks: Methods, Models, and Architectures for Network Security
Robust DWT-SVD domain image watermarking: embedding data in all frequencies
Proceedings of the 2004 workshop on Multimedia and security
Review: A variation on SVD based image compression
Image and Vision Computing
A semi-blind digital watermarking scheme based on singular value decomposition
Computer Standards & Interfaces
IEEE Transactions on Image Processing
Robust digital image watermarking in DWT-SVD domain
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Progressive image transmission using singular value decomposition
EGMM'04 Proceedings of the Seventh Eurographics conference on Multimedia
A digital watermarking scheme based on singular value decomposition
ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
Lossy image compression using singular value decomposition and wavelet difference reduction
Digital Signal Processing
| Hi-index | 0.01 |
The combination of singular value decomposition (SVD) and vector quantization (VQ) is proposed as a compression technique to achieve low bit rate and high quality image coding. Given a codebook consisting of singular vectors, two algorithms, which find the best-fit candidates without involving the complicated SVD computation, are described. Simulation results show that the proposed methods are better than the discrete cosine transform (DCT) in terms of energy compaction, data rate, image quality, and decoding complexity