상세정보
학술지
Regression-based prediction for blocking artifact reduction in JPEG-compressed images
- 저자
- 이기륭, 김동식, Taejeong Kim
- 발행일
- 200501
- 출처
- IEEE Transactions on Image Processing, v.14 no.1, pp.36-48
- ISSN
- 1057-7149
- 출판사
- IEEE
- 협약과제
- 04MC1200, 지능형 웹스파이더를 이용한 불법 복제 콘텐츠 추적기술, 서영호
- 초록
- In order to reduce the blocking artifact in the Joint Photographic Experts Group (JPEG)-compressed images, a new noniterative postprocessing algorithm is proposed. The algorithm consists of a two-step operation: low-pass filtering and then predicting. Predicting the original image from the low-pass filtered image is performed by using the predictors, which are constructed based on a broken line regression model. The constructed predictor is a generalized version of the projector onto the quantization constraint set [21], [23], or the narrow quantization constraint set [10]. We employed different predictors depending on the frequency components in the discrete cosine transform (DCT) domain since each component has different stafistical properties. Further, by using a simple classifier, we adaptively applied the predictors depending on the local variance of the DCT block. This adaptation enables an appropriate blurring depending on the smooth or detail region, and shows improved performance in terms of the average distortion and the perceptual view. For the major-edge DCT blocks, which usually suffer from the ringing artifact, the quality of fit to the regression model is usually not good. By making a modification of the regression model for such DCT blocks, we can also obtain a good perceptual view. The proposed algorithm does not employ any sophisticated edge-oriented classifiers and nonlinear filters. Compared to the previously proposed algorithms, the proposed algorithm provides comparable or better results with less computational complexity. © 2005 IEEE.
- KSP 제안 키워드
- Blocking artifact reduction, Computational complexity, Constraint set, Discrete cosine Transform, Edge-oriented, Frequency components, Joint Photographic Experts Group(JPEG), Local Variance, Nonlinear Filters, Regression Model, Regression-based