Compressing still and moving images with wavelets
Multimedia Systems - Special issue on video compression
Mixtures of probabilistic principal component analyzers
Neural Computation
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
ICALP '89 Proceedings of the 16th International Colloquium on Automata, Languages and Programming
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rank-R Approximation of Tensors: Using Image-as-Matrix Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Generalized Low Rank Approximations of Matrices
Machine Learning
Learning Overcomplete Representations
Neural Computation
Review: A variation on SVD based image compression
Image and Vision Computing
Neural Computation
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Compression of color facial images using feature correction two-stage vector quantization
IEEE Transactions on Image Processing
Higher Order SVD Analysis for Dynamic Texture Synthesis
IEEE Transactions on Image Processing
Combined techniques of singular value decomposition and vector quantization for image coding
IEEE Transactions on Image Processing
A novel gray image representation using overlapping rectangular NAM and extended shading approach
Journal of Visual Communication and Image Representation
Learning based compression for real-time rendering of surface light fields
ACM SIGGRAPH 2013 Posters
SIGGRAPH Asia 2013 Technical Briefs
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We present a new method for compact representation of large image datasets. Our method is based on treating small patches from a 2-D image as matrices as opposed to the conventional vectorial representation, and encoding these patches as sparse projections onto a set of exemplar orthonormal bases, which are learned a priori from a training set. The end result is a low-error, highly compact image/patch representation that has significant theoretical merits and compares favorably with existing techniques (including JPEG) on experiments involving the compression of ORL and Yale face databases, as well as a database of miscellaneous natural images. In the context of learning multiple orthonormal bases, we show the easy tunability of our method to efficiently represent patches of different complexities. Furthermore, we show that our method is extensible in a theoretically sound manner to higher-order matrices ("tensors"). We demonstrate applications of this theory to compression of well-known color image datasets such as the GaTech and CMU-PIE face databases and show performance competitive with JPEG. Lastly, we also analyze the effect of image noise on the performance of our compression schemes.