Algorithm for JPEG artifact reduction via local edge regeneration

Golestaneh, S. Alireza; Chandler, Damon M.

doi:10.1117/1.jei.23.1.013018

Cited by 28 publications

(20 citation statements)

References 46 publications

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“…In addition, a comparison was carried out with four representative methods appearing in the literature (Chen et al, 7 Kim et al, 13 Kim and Sim, 15 and Golestaneh and Chandler 17 ) that have been shown to generate superior performance over other existing methods. In addition, a comparison was carried out with four representative methods appearing in the literature (Chen et al, 7 Kim et al, 13 Kim and Sim, 15 and Golestaneh and Chandler 17 ) that have been shown to generate superior performance over other existing methods.…”

Section: Comparison Resultsmentioning

confidence: 99%

Optimization method to reduce blocking artifacts in JPEG images

Pourreza-Shahri

Yousefi

Kehtarnavaz

2014

J. Electron. Imaging

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This paper presents an optimization method to reduce blocking artifacts in JPEG images by utilizing the image gradient information. A closed-form solution is derived for the optimization method. To address the computational feasibility aspect of the large matrices involved in the closed-form solution, a sliding window approach is devised. The performance of the developed method is compared with several blocking artifacts reduction methods in the literature and also with the deblocking filter deployed in high efficiency video coding by examining the three measures of peak signal-to-noise ratio, generalized block-edge impairment metric (M GBIM ), and structural similarity. The comparison results indicate the effectiveness of the introduced method in particular for low bit-rate JPEG images.

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Section: Comparison Resultsmentioning

confidence: 99%

Optimization method to reduce blocking artifacts in JPEG images

Pourreza-Shahri

Yousefi

Kehtarnavaz

2014

J. Electron. Imaging

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“…The experimental results demonstrate that the proposed method outperforms the existing metrics. Our future work related to the proposed metric includes the design of blocking artifacts alleviation algorithms [23], [24]. How to evaluate the quality of deblocked images is another interesting topic [25].…”

Section: Discussionmentioning

confidence: 99%

Learning Structural Regularity for Evaluating Blocking Artifacts in JPEG Images

Wang

Zhu

2014

IEEE Signal Process. Lett.

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Image degradation damages genuine visual structures and causes pseudo structures. Pseudo structures are usually present with regularities. This letter proposes a machine learning based blocking artifacts metric for JPEG images by measuring the regularities of pseudo structures. Image corner, block boundary and color change properties are used to differentiate the blocking artifacts. A support vector regression (SVR) model is adopted to learn the underlying relations between these features and perceived blocking artifacts. The blocking artifacts score of a test image is predicted using the trained model. Extensive experiments demonstrate the effectiveness of the method.Index Terms-Blocking artifacts, image quality assessment, structural regularity, support vector regression (SVR).

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“…In [14,15], a feature is derived from the normalized energy density and then SVM is used to robustly detect resampled images. Some recent approaches [17,21] have been proposed to reduce the JPEG artifact left by compression.…”

Section: Related Workmentioning

confidence: 99%

Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning

Bunk

Bappy

Mohammed

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

153

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Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries.

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Algorithm for JPEG artifact reduction via local edge regeneration

Cited by 28 publications

References 46 publications

Optimization method to reduce blocking artifacts in JPEG images

Optimization method to reduce blocking artifacts in JPEG images

Learning Structural Regularity for Evaluating Blocking Artifacts in JPEG Images

Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning

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