Vector quantization and signal compression
Vector quantization and signal compression
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A region-based scheme using RKLT and predictive classified vector quantization
Computer Vision and Image Understanding
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Atomic Decomposition by Basis Pursuit
SIAM Review
An automatic system for model-based coding of faces
DCC '95 Proceedings of the Conference on Data Compression
Model-Based Multi-Stage Compression of Human Face Images
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Segmentation based coding of human face images for retrieval
Signal Processing
Sparse Image Coding Using a 3D Non-Negative Tensor Factorization
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Image compression using an edge adapted redundant dictionary and wavelets
Signal Processing - Sparse approximations in signal and image processing
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Just relax: convex programming methods for identifying sparse signals in noise
IEEE Transactions on Information Theory
Vector quantization of image subbands: a survey
IEEE Transactions on Image Processing
Compression of color facial images using feature correction two-stage vector quantization
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Low Bit-Rate Compression of Facial Images
IEEE Transactions on Image Processing
Manifold models for signals and images
Computer Vision and Image Understanding
One Graph Is Worth a Thousand Logs: Uncovering Hidden Structures in Massive System Event Logs
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
IEEE Transactions on Image Processing
Double sparsity: learning sparse dictionaries for sparse signal approximation
IEEE Transactions on Signal Processing
Bayesian orthogonal component analysis for sparse representation
IEEE Transactions on Signal Processing
Large scale visual classification via learned dictionaries and sparse representation
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Pixel-level image fusion with simultaneous orthogonal matching pursuit
Information Fusion
Image primitive coding and visual quality assessment
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
SIGGRAPH Asia 2013 Technical Briefs
Online Dictionary Learning Based Intra-frame Video Coding
Wireless Personal Communications: An International Journal
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The use of sparse representations in signal and image processing is gradually increasing in the past several years. Obtaining an overcomplete dictionary from a set of signals allows us to represent them as a sparse linear combination of dictionary atoms. Pursuit algorithms are then used for signal decomposition. A recent work introduced the K-SVD algorithm, which is a novel method for training overcomplete dictionaries that lead to sparse signal representation. In this work we propose a new method for compressing facial images, based on the K-SVD algorithm. We train K-SVD dictionaries for predefined image patches, and compress each new image according to these dictionaries. The encoding is based on sparse coding of each image patch using the relevant trained dictionary, and the decoding is a simple reconstruction of the patches by linear combination of atoms. An essential pre-process stage for this method is an image alignment procedure, where several facial features are detected and geometrically warped into a canonical spatial location. We present this new method, analyze its results and compare it to several competing compression techniques.