Artifact reduction of compressed images and video combining adaptive fuzzy filtering and directional anisotropic diffusion

Nadernejad, Ehsan; Forchhammer, Søren; Korhonen, Jari

doi:10.1109/euvip.2011.6045551

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…Enhancing image and video while preserving their details is very important. Thus the authors in [2], [13], 1859-378X-2019-3403 c 2019 REV [14], [15], [16], and [17] use edge maps ( [17] and [18]) to adapt filters' strength. However, the pixel classification is rather complicated and may lead to error.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Texture Map Based Artifact Reduction For Compressed Videos

Thai¹,

Do-Hong²,

Dung³

2020

REV J. Electron. Commun.

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This paper proposes a method of artifact reduction in compressed videos using dynamic texture map together with artifact maps and 3D - fuzzy filters. To preserve better details during filtering process, the authors introduce a novel method to construct a texture map for video sequences called dynamic texture map. Then, temporal arifacts such as flicker artifacts and mosquito artifacts are also estimated by advanced flicker maps and mosquito maps. These maps combined with fuzzy filters are applied to intraframe and interframe pixels to enhancecompressed videos. Simulation results verify the advanced performance of the proposed fuzzy filtering scheme in term of visual quality, SSIM, PSNR and flicker metrics in comparisionwith existing state of the art methods.

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Section: Introductionmentioning

confidence: 99%

Dynamic Texture Map Based Artifact Reduction For Compressed Videos

Thai¹,

Do-Hong²,

Dung³

2020

REV J. Electron. Commun.

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“…To deblock artifacts, the authors in [16] propose method to detect pixels suffering from blocking artifacts and only deblock these pixels. In block-based compression, blocking artifacts occur at the boundary of blocks along vertical and horizontal directions.…”

Section: Blocking Map and Ringing Mapmentioning

confidence: 99%

“…The Beltrami method in [14], [15] is used to locate texture for image segmentation. Recently a texture map is estimated using the enhanced Beltrami method in [16] to improve the noise resistance of the estimation. For the first time, the authors in [17] utilize the texture map to remove blocking artifacts only.…”

Section: Introductionmentioning

confidence: 99%

A Novel Joint Blocking and Ringing Artifact Reduction using Advanced Beltrami Based Texture Maps

Nguyen¹,

Hong²,

Võ³

2017

REV J. Electron. Commun.

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Abstract-This paper proposes a novel image enhancement approach using advanced texture maps together with isotropic or directional fuzzy filters. Texture maps of original images or compressed images are estimated and then are used to control the filter's strength. The aim is to reduce blocking and ringing artifacts while preserving the sharpness of compressed images. The spread parameter of fuzzy filters plays an important role to control deblocking and deringing process. Thus, the spread parameter is optimized to obtain the highest image quality. The proposed algorithm also simultaneously combines deblocking and deringing schemes to reduce the algorithm's complexity. Simulation results show that the proposed fuzzy filtering scheme achieves the best visual quality, SSIM, and PSNR values among existing methods.

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“…In artifact removal using fuzzy filtering method [8], a combination of fuzzy filtering and the powerful anisotropic diffusion equations is employed. It is known that the selection of adaptive threshold parameter that forms a diffusion coefficient has also improved performance of the algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

A Machine Learning Approach for Removal of JPEG Compression Artifacts: A Survey

Anagha¹,

Kavya²,

Namratha³

et al. 2016

IJCA

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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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Artifact reduction of compressed images and video combining adaptive fuzzy filtering and directional anisotropic diffusion

Cited by 11 publications

References 14 publications

Dynamic Texture Map Based Artifact Reduction For Compressed Videos

Dynamic Texture Map Based Artifact Reduction For Compressed Videos

A Novel Joint Blocking and Ringing Artifact Reduction using Advanced Beltrami Based Texture Maps

A Machine Learning Approach for Removal of JPEG Compression Artifacts: A Survey

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