Research Article
A Machine Learning Approach for Removal of JPEG Compression Artifacts: A Survey
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 138 - Issue 2 |
| Published: March 2016 |
| Authors: Anagha R., Kavya B., Namratha M., Chandralekha Singasani, Hamsa J. |
10.5120/ijca2016908732
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Anagha R., Kavya B., Namratha M., Chandralekha Singasani, Hamsa J. . A Machine Learning Approach for Removal of JPEG Compression Artifacts: A Survey. International Journal of Computer Applications. 138, 2 (March 2016), 24-28. DOI=10.5120/ijca2016908732
@article{ 10.5120/ijca2016908732,
author = { Anagha R.,Kavya B.,Namratha M.,Chandralekha Singasani,Hamsa J. },
title = { A Machine Learning Approach for Removal of JPEG Compression Artifacts: A Survey },
journal = { International Journal of Computer Applications },
year = { 2016 },
volume = { 138 },
number = { 2 },
pages = { 24-28 },
doi = { 10.5120/ijca2016908732 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2016
%A Anagha R.
%A Kavya B.
%A Namratha M.
%A Chandralekha Singasani
%A Hamsa J.
%T A Machine Learning Approach for Removal of JPEG Compression Artifacts: A Survey%T
%J International Journal of Computer Applications
%V 138
%N 2
%P 24-28
%R 10.5120/ijca2016908732
%I Foundation of Computer Science (FCS), NY, USA
Abstract
JPEG is a widely used image compression method. Though it is very efficient, it introduces certain artifacts and quantization noise. This paper is a detailed survey about various existing methods for the reduction of these artifacts. The paper explains each method and their advantages and drawbacks. Some of the methods mentioned are Weiner filtering, Image Optimization, Zero-masking, Local Edge regeneration, Multiple dictionary learning, Hybrid Filtering, Fuzzy filtering, Total Variation Regularization, Offset and Shift Technique, Post-processing et al. Also, a comparative study is made as to which method is suitable for which scenario.
References
- S.Gayathri Tejaswini, M. Ramalakshmi, M. Santhi, H. Rahul and Hemanth Nag, “Reduction of Blocking Artifacts of DCT Compressed Image Based on Block Wiener Filtering”, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 4, Issue 3, March 2015.
- Dr.S.S.Pandey, Manu Pratap Singh and Vikas Pandey, ”Block wise image compression & Reduced Blocks Artifacts Using Discrete Cosine Transform”, International Journal of Scientific and Research Publications, Volume 5, Issue 3, March 2015.
- Reza Pourreza-Shahri, Siamak Yousefi and Nasser Kehtarnavaz, “Optimization method to reduce blocking artifacts in JPEG images”, Journal of Electronic Imaging, November 2014.
- Jyothi Mishra, “Suppression of Blocking Artifacts in Compressed Images”, Project Thesis, NIT Rourkela, May 2014.
- S. AlirezaGolestaneh and Damon M. Chandler, “Algorithm for JPEG artifact reduction via local edge regeneration”, Journal of Electronic Imaging, January 2014.
- Yi Wang and Faith Porikli, “Multiple Dictionary Learning for Blocking Artifacts Reduction”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012.
- Tuan Q. Pham and Lucas J. van Vliet, “Blocking artifacts removal by a hybrid filter method”.
- EhsanNadernejad, SørenForchhammer, and JariKorhonen, “Artifact Reduction Of Compressed Images And Video Combining Adaptive Fuzzy Filtering And Directional Anisotropic Diffusion”, 3rd European Workshop on Visual Information Processing (EUVIP), 2011.
- Fang Zhu, “Blocking Artifacts Reduction in Compressed Data”,International Conference on Computer Engineering and Applications, 2009.
- Chitra PS, Niyas Ibrahim and Dr. A NeelaMadheshwari, “Artifacts Removal in JPEG Decompression via learned dictionary and Total variation regularization”, International Conference on Current Techniques in Medical Image Analysis, 2014.
- Jagroop Singh, Sukhwinder Singh and Dilbag Singh, “Reduction of blocking artifacts by post filtering algorithm”, Journal of Information and Computing Science,2013.
- Bobby Lukose and P. Roobini, “A Post Processing Approach for Ringing-Artifact Reduction”, IJISET - International Journal of Innovative Science, Engineering & Technology, September 2014.
- Ling Shao, Jingnan Wang, IhorKirenko and Gerard de Haan, “Quality Adaptive Trained Filters For Compression Artifacts Removal”, IEEE 2008.
- YuvinderDandiwal, KirtiSachdeva, “The Study Of Various Approaches For Removal Of Blocking Artifacts In Spatial Domain”, International Journal of Computer Science and Mobile Computing, Vol.2 Issue. 12, December 2013.
- BasakOztan, Amal Malik, Zhigang Fan and Reiner Eschbach, “Removal of Artifacts from JPEG Compressed Document Images”, SPIE- IS&T.
- M. Hanmandlu. J. See, S. Vasikarla, “Fuzzy Edge Detector Using Entropy Optimization”, Proceedings of the International Conference on InformationTechnology: Coding and Computing, 2004.
- Y. Luo, R.K. Ward, “Removing the Blocking Artifacts of Block-Based DCT Compressed Images”, IEEE Trans. Image Processing, 12, 2003.
- S. Singh, V. Kumar, H.K. Verma, “Reduction of blocking artifacts in JPEG Compressed Images”, Digital Signal Processing, 17, 2007.
- J. Kim and Chun-Bo Sim, “Compression Artifacts Removal by Signal Adaptive Weighted Sum Technique”, IEEE Transactions on Consumer Electronics, November 2011.
- Amir Z. Averbuch, A.Schclar and David L. Donoho, “Deblocking of block-transform compressed images using weighted sums of symmetrically aligned pixels”, IEEE Trans. Image Process., 2005.
- Chun-Su park, J. Hyungkim and Sung-Jeako, “Fast Blind Measurement of Blocking Artifacts in both Pixel and DCT domain”, Journal of Mathematical Imaging and Vision, 2007.
- Yen-Yu Chen, Ying-Wen Chang and Wen-Chien Yen, “Design a deblocking filter with three separate modes in DCT – based coding”, Journal of Visual Communication and Image processing, 2008.
- G.Zhai, W.Znag, X-Yang and W.Lin, “Efficient image deblocking based on post filtering in shifted windows”, IEEE Transactions Circuits Systems & Video Technology, 2008.
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