IEICE TRANSACTIONS on Information
Application of Content Specific Dictionaries in Still Image Coding
Summary :
The concept of sparse representation is gaining momentum in image processing applications, especially in image compression, from last one decade. Sparse coding algorithms represent signals as a sparse linear combination of atoms of an overcomplete dictionary. Earlier works shows that sparse coding of images using learned dictionaries outperforms the JPEG standard for image compression. The conventional method of image compression based on sparse coding, though successful, does not adapting the compression rate based on the image local block characteristics. Here, we have proposed a new framework in which the image is classified into three classes by measuring the block activities followed by sparse coding each of the classes using dictionaries learned specific to each class. K-SVD algorithm has been used for dictionary learning. The sparse coefficients for each class are Huffman encoded and combined to form a single bit stream. The model imparts some rate-distortion attributes to compression as there is provision for setting a different constraint for each class depending on its characteristics. We analyse and compare this model with the conventional model. The outcomes are encouraging and the model makes way for an efficient sparse representation based image compression.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.2 pp.394-403
- Publication Date
- 2015/02/01
- Publicized
- 2014/11/10
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7135
- Type of Manuscript
- PAPER
- Category
- Image Processing and Video Processing
Authors
Jigisha N PATEL
Sardar Vallabhbhai National Institute of Technology
Jerin JOSE
Sardar Vallabhbhai National Institute of Technology
Suprava PATNAIK
Xavier Institute of Engineering
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Jigisha N PATEL, Jerin JOSE, Suprava PATNAIK, "Application of Content Specific Dictionaries in Still Image Coding" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 2, pp. 394-403, February 2015, doi: 10.1587/transinf.2014EDP7135.
Abstract: The concept of sparse representation is gaining momentum in image processing applications, especially in image compression, from last one decade. Sparse coding algorithms represent signals as a sparse linear combination of atoms of an overcomplete dictionary. Earlier works shows that sparse coding of images using learned dictionaries outperforms the JPEG standard for image compression. The conventional method of image compression based on sparse coding, though successful, does not adapting the compression rate based on the image local block characteristics. Here, we have proposed a new framework in which the image is classified into three classes by measuring the block activities followed by sparse coding each of the classes using dictionaries learned specific to each class. K-SVD algorithm has been used for dictionary learning. The sparse coefficients for each class are Huffman encoded and combined to form a single bit stream. The model imparts some rate-distortion attributes to compression as there is provision for setting a different constraint for each class depending on its characteristics. We analyse and compare this model with the conventional model. The outcomes are encouraging and the model makes way for an efficient sparse representation based image compression.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7135/_p
Copy
@ARTICLE{e98-d_2_394,
author={Jigisha N PATEL, Jerin JOSE, Suprava PATNAIK, },
journal={IEICE TRANSACTIONS on Information},
title={Application of Content Specific Dictionaries in Still Image Coding},
year={2015},
volume={E98-D},
number={2},
pages={394-403},
abstract={The concept of sparse representation is gaining momentum in image processing applications, especially in image compression, from last one decade. Sparse coding algorithms represent signals as a sparse linear combination of atoms of an overcomplete dictionary. Earlier works shows that sparse coding of images using learned dictionaries outperforms the JPEG standard for image compression. The conventional method of image compression based on sparse coding, though successful, does not adapting the compression rate based on the image local block characteristics. Here, we have proposed a new framework in which the image is classified into three classes by measuring the block activities followed by sparse coding each of the classes using dictionaries learned specific to each class. K-SVD algorithm has been used for dictionary learning. The sparse coefficients for each class are Huffman encoded and combined to form a single bit stream. The model imparts some rate-distortion attributes to compression as there is provision for setting a different constraint for each class depending on its characteristics. We analyse and compare this model with the conventional model. The outcomes are encouraging and the model makes way for an efficient sparse representation based image compression.},
keywords={},
doi={10.1587/transinf.2014EDP7135},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Application of Content Specific Dictionaries in Still Image Coding
T2 - IEICE TRANSACTIONS on Information
SP - 394
EP - 403
AU - Jigisha N PATEL
AU - Jerin JOSE
AU - Suprava PATNAIK
PY - 2015
DO - 10.1587/transinf.2014EDP7135
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2015
AB - The concept of sparse representation is gaining momentum in image processing applications, especially in image compression, from last one decade. Sparse coding algorithms represent signals as a sparse linear combination of atoms of an overcomplete dictionary. Earlier works shows that sparse coding of images using learned dictionaries outperforms the JPEG standard for image compression. The conventional method of image compression based on sparse coding, though successful, does not adapting the compression rate based on the image local block characteristics. Here, we have proposed a new framework in which the image is classified into three classes by measuring the block activities followed by sparse coding each of the classes using dictionaries learned specific to each class. K-SVD algorithm has been used for dictionary learning. The sparse coefficients for each class are Huffman encoded and combined to form a single bit stream. The model imparts some rate-distortion attributes to compression as there is provision for setting a different constraint for each class depending on its characteristics. We analyse and compare this model with the conventional model. The outcomes are encouraging and the model makes way for an efficient sparse representation based image compression.
ER -
English
