Premium
Support Vector Machine (SVM) based compression artifact‐reduction technique
Author(s) -
Biswas Mainak,
Kumar Sanjeev,
Nguyen T. Q.,
Balram Nikhil
Publication year - 2007
Publication title -
journal of the society for information display
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 52
eISSN - 1938-3657
pISSN - 1071-0922
DOI - 10.1889/1.2770864
Subject(s) - computer science , artifact (error) , artificial intelligence , compression artifact , support vector machine , discrete cosine transform , vector quantization , undo , reduction (mathematics) , ringing artifacts , quantization (signal processing) , algorithm , pattern recognition (psychology) , data compression , computer vision , image compression , mathematics , image processing , image (mathematics) , geometry , operating system
— A compression artifact‐reduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise‐corrupted version. As opposed to artifact‐reduction methods specific to each type of compression artifact ( e.g. , blocking, ringing, etc. ), we treat such artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and artifact‐corrupted image, determined during the training step. Experimental results exhibit significant reduction in all types of compression artifacts.