Probabilistic compression artifacts reduction using self-similarity based noise region estimation

Oh Young Lee; Je Ho Ryu; Jong-Ok Kim

doi:10.1109/APSIPA.2015.7415379

Probabilistic compression artifacts reduction using self-similarity based noise region estimation

Oh Young Lee, Je Ho Ryu, Jong-Ok Kim

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

During compression artifact reduction process, original information as well as noise has been commonly removed, and this side effect should be importantly considered. In this paper, we propose a novel post-processing approach to alleviate the side effect of noise reduction while still reducing compression artifacts successfully. After compression artifact removal using conventional methods, we examine whether the denoised region is actually noisy or not, exploiting the relationship between noisy image and artifact reduced image. Then, the probability of a pixel to be noisy is calculated based on the noise region estimation, and a final denoised pixel is obtained by a weighted average between noisy and denoised signals with the probability. Experimental results show that the proposed method is more effective in preserving texture region while still reducing the compression noise.

Original language	English
Title of host publication	2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	784-788
Number of pages	5
ISBN (Electronic)	9789881476807
DOIs	https://doi.org/10.1109/APSIPA.2015.7415379
Publication status	Published - 2016 Feb 19
Event	2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, Hong Kong Duration: 2015 Dec 16 → 2015 Dec 19

Other

Other	2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
Country/Territory	Hong Kong
City	Hong Kong
Period	15/12/16 → 15/12/19

Keywords

Compression Artifact Reduction
Noise Region Estimation
Probabilistic Noise Removal
Self-Similarity

ASJC Scopus subject areas

Artificial Intelligence
Modelling and Simulation
Signal Processing

Access to Document

10.1109/APSIPA.2015.7415379

Cite this

Lee, O. Y., Ryu, J. H., & Kim, J-O. (2016). Probabilistic compression artifacts reduction using self-similarity based noise region estimation. In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 (pp. 784-788). [7415379] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2015.7415379

Probabilistic compression artifacts reduction using self-similarity based noise region estimation. / Lee, Oh Young; Ryu, Je Ho; Kim, Jong-Ok.

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 784-788 7415379.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, OY, Ryu, JH & Kim, J-O 2016, Probabilistic compression artifacts reduction using self-similarity based noise region estimation. in 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015., 7415379, Institute of Electrical and Electronics Engineers Inc., pp. 784-788, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015, Hong Kong, Hong Kong, 15/12/16. https://doi.org/10.1109/APSIPA.2015.7415379

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abstract = "During compression artifact reduction process, original information as well as noise has been commonly removed, and this side effect should be importantly considered. In this paper, we propose a novel post-processing approach to alleviate the side effect of noise reduction while still reducing compression artifacts successfully. After compression artifact removal using conventional methods, we examine whether the denoised region is actually noisy or not, exploiting the relationship between noisy image and artifact reduced image. Then, the probability of a pixel to be noisy is calculated based on the noise region estimation, and a final denoised pixel is obtained by a weighted average between noisy and denoised signals with the probability. Experimental results show that the proposed method is more effective in preserving texture region while still reducing the compression noise.",

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N2 - During compression artifact reduction process, original information as well as noise has been commonly removed, and this side effect should be importantly considered. In this paper, we propose a novel post-processing approach to alleviate the side effect of noise reduction while still reducing compression artifacts successfully. After compression artifact removal using conventional methods, we examine whether the denoised region is actually noisy or not, exploiting the relationship between noisy image and artifact reduced image. Then, the probability of a pixel to be noisy is calculated based on the noise region estimation, and a final denoised pixel is obtained by a weighted average between noisy and denoised signals with the probability. Experimental results show that the proposed method is more effective in preserving texture region while still reducing the compression noise.

AB - During compression artifact reduction process, original information as well as noise has been commonly removed, and this side effect should be importantly considered. In this paper, we propose a novel post-processing approach to alleviate the side effect of noise reduction while still reducing compression artifacts successfully. After compression artifact removal using conventional methods, we examine whether the denoised region is actually noisy or not, exploiting the relationship between noisy image and artifact reduced image. Then, the probability of a pixel to be noisy is calculated based on the noise region estimation, and a final denoised pixel is obtained by a weighted average between noisy and denoised signals with the probability. Experimental results show that the proposed method is more effective in preserving texture region while still reducing the compression noise.

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Probabilistic compression artifacts reduction using self-similarity based noise region estimation

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Keywords

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