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Zhao, C., Zhang, J., Ma, S., Fan, X., Zhang, Y. and Gao, W. (2017) Reducing Image Compression Artifacts by Structural Sparse Representation and Quantization Constraint Prior. IEEE Transactions on Circuits and Systems for Video Technology, 27, 2057-2071.
https://doi.org/10.1109/TCSVT.2016.2580399

has been cited by the following article:

• TITLE: Adaptive Lossy Image Compression Based on Singular Value Decomposition

  AUTHORS: Marcos Roberto e Souza, Helio Pedrini

  KEYWORDS: Image Compression, Adaptive Decomposition, Lossy Compression

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.10 No.3, August 8, 2019

  ABSTRACT: Image compression techniques aim to reduce redundant information in order to allow data storage and transmission in an efficient way. In this work, we propose and analyze a lossy image compression method based on the singular value decomposition using an optimal choice of eigenvalues and an adaptive mechanism for block partitioning. Experiments are conducted on several images to demonstrate the effectiveness of the proposed compression method in comparison with the direct application of the singular value decomposition.