Paper Info
Title
Globally Variance-Constrained Sparse Representation for Image Set Compression.
Abstract
Sparse representation presents an efficient approach to approximately recover a signal by the linear composition of a few bases from a learnt dictionary, based on which various successful applications have been observed. However, in the scenario of data compression, its efficiency and popularity are hindered due to the extra overhead for encoding the sparse coefficients. Therefore, how to establish an accurate rate model in sparse coding and dictionary learning becomes meaningful, which has been not fully exploited in the context of sparse representation. According to the Shannon entropy inequality, the variance of data source bounds its entropy, which can reflect the actual coding bits. Hence, in this work a Globally Variance-Constrained Sparse Representation (GVCSR) model is proposed, where a variance-constrained rate model is introduced in the optimization process. Specifically, we employ the Alternating Direction Method of Multipliers (ADMM) to solve the non-convex optimization problem for sparse coding and dictionary learning, both of which have shown state-of-the-art performance in image representation. Furthermore, we investigate the potential of GVCSR in practical image set compression, where a common dictionary is trained by several key images to represent the whole image set. Experimental results have demonstrated significant performance improvements against the most popular image codecs including JPEG and JPEG2000.
Year
Venue
DocType
2016
CoRR
Journal
Volume
Citations 
PageRank 
abs/1608.04902
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Xiang Zhang18812.61
Jiarui Sun200.68
Siwei Ma32229203.42
Zhouchen Lin44805203.69
Jian Zhang5595112.97
Shiqi Wang61281120.37
Wen Gao722334.51