The JPEG still picture compression standard
Communications of the ACM - Special issue on digital multimedia systems
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Inpainting and Zooming Using Sparse Representations
The Computer Journal
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
IEEE Transactions on Multimedia
IEEE Transactions on Consumer Electronics
Adaptive blocking artifact reduction using wavelet-based block analysis
IEEE Transactions on Consumer Electronics
New edge-directed interpolation
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Sparse Representation for Color Image Restoration
IEEE Transactions on Image Processing
Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data
IEEE Transactions on Image Processing
Quality Assessment of Deblocked Images
IEEE Transactions on Image Processing
Efficient Image Deblocking Based on Postfiltering in Shifted Windows
IEEE Transactions on Circuits and Systems for Video Technology
Image deblocking via sparse representation
Image Communication
Computationally Efficient Formulation of Sparse Color Image Recovery in the JPEG Compressed Domain
Journal of Mathematical Imaging and Vision
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The representation model that considers an image as a sparse linear combination of few atoms of a predefined or learned dictionary has received considerable attention in recent years. Among the others, the Structured Sparse Model Selection (SSMS) was recently introduced. This model outperforms different state-of-the-art algorithms in a number of imaging tasks (e.g., denoising, deblurring, inpainting). Despite the high denoising performances achieved by SSMS have been demonstrated, the compression issues has been not considered during the evaluation. In this paper we study the performances of SSMS under lossy JPEG compression. Experiments have shown that the SSMS method is able to restore compressed noisy images with a significant margin, both in terms of PSNR and SSIM quality measure, even though the original framework is not tuned for the specific task of compression. Quantitative and qualitative results pointed out that SSMS is able to perform both denoising and compression artifacts reduction (e.g., deblocking), by demonstrating the promise of sparse coding methods in application where different computational engines are combined to generate a signal (e.g., Imaging Generation Pipeline of single sensor devices).