Compression of facial images using the K-SVD algorithm

By Ori Bryt, Michael Elad. Journal of Visual Communication and Image Representation, 2008

346 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/372d470555112ca147a83a125aba7431bb53ecaa

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In Computer Vision and Pattern Recognition

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Citations in the corpus

From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images (2009, background): “More details on these and other experiments can be found in [9], and here we concentrate on showing the main results. where y ( i, j ) are the original image pixels and ˆ y ( i, j ) are the compressed-decompressed ones.”
Sparse Modeling for Image and Vision Processing (2014, methodology): “Unlike generic approaches such as JPEG and JPEG2000, the approach of Bryt and Elad [2008] can exploit the fact that each patch represent a specific information, e.”
Sparse Representation for Computer Vision and Pattern Recognition (2010, background): “, [17], [20], [30], have been developed in recent years, leading to state-ofthe-art results in many signal and image processing tasks [11], [32], [44], [48], [51], [54].”
Image compression with Stochastic Winner-Take-All Auto-Encoder (2017, neutral): “SWTA AE is similar to Orthogonal Matching Pursuit (OMP) [15], a common algorithm for image compression using sparse representations [16].”
Learning hierarchical sparse features for RGB-(D) object recognition (2014, background): “…approach has lead to the state-of-the-art results in various applications such as image denoising (Elad and Aharon, 2006b), face image compression (Bryt and Elad, 2008), texture segmentation (Mairal et al., 2008a), and contour detection (Ren and Bo, 2012 Here, N is the size of the training data,…”
The student-t mixture as a natural image patch prior with application to image compression (2014, background): “Image compression based on sparse coding has been proposed (Horev et al., 2012) for images in general (Bryt and Elad, 2008; Zepeda et al., 2011) and for a specific class of facial images.”
Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization (2009, methodology): “We start by reviewing prior related work, following by the presentation of our proposed algorithm and the first results showing the computational and reconstruction advantages of this new approach.”
Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation (2010, methodology): “Unfortunately, adaptive methods usually prefer explicit dictionary representations over structured ones, gaining a higher degree of freedom in the training but sacrificing regularity and efficiency of the result.2 Bridging this gap between complexity and adaptivity requires a parametric dictionary…”
On the Local Correctness of L^1 Minimization for Dictionary Learning (2011, background): “Exploiting sparsity in learned dictionaries has led to practical success in a number of important problems in signal acquisition and processing [EA06, BE08, MBP + 08, RS08, YWHM10].”
Discriminative K-SVD for dictionary learning in face recognition (2010, background): “Recently, a lot of attention has been given to applying sparse-representation-based techniques to computer vision and image processing problems, such as image denoising [10], image inpainting [16], image compression [4][5].”
Fisher Discrimination Dictionary Learning for sparse representation (2011, methodology): “The past several years have witnessed the rapid development of the theory and algorithms of sparse representation (or coding) [30] and its successful applications in image restoration [1] [2] [3] and compressed sensing [4].”
Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification (2014, methodology): “Instead, learning the desired dictionaries from the training data with sparsity regularization has led to state-of-the-art results in image reconstruction (Elad and Aharon 2006; Bryt and Elad 2008; Aharon et al. 2006; Mairal et al. 2012; Zhou et al. 2012) and image classification (Mairal et al.”
Pixel-level image fusion with simultaneous orthogonal matching pursuit (2012, background): “Applications that benefit from the sparsity and overcompleteness include compression, regularization in inverse problems [29], image and video compression [30,31], computer vision tasks and so on [32].”
Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit (2008, methodology): “In a common image processing task, for instance, the image could be decomposed into thousands of (possibly overlapping) image patches, with each patch undergoing one or more sparse-coding processes ([10, 11, 12, 13, 14] and others).”