The week's pick
Reseach Article
A Machine Learning Approach for Removal of JPEG Compression Artifacts: A Survey
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 138 - Number 2 |
| Year of Publication: 2016 |
| Authors: Anagha R., Kavya B., Namratha M., Chandralekha Singasani, Hamsa J. |
10.5120/ijca2016908732
|
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
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.
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