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Enhanced image compression with global adaptive loop filter and machine learning-based optimization.

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Title: Enhanced image compression with global adaptive loop filter and machine learning-based optimization.
Authors: Hu, Jianhua1 (AUTHOR), Wu, Weimei1 (AUTHOR) 120760750@qq.com, Wang, Bo1 (AUTHOR), Yang, Jiahui1 (AUTHOR), Liu, Xiaolin1 (AUTHOR), Luo, Guixiang2 (AUTHOR)
Source: Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.). Jan2025, Vol. 25 Issue 1, p270-281. 12p.
Subject Terms: *JPEG (Image coding standard), *Adaptive filters, *Deep learning, *Contrast effect, *Machine learning, *Image compression
Abstract: Lossy compression often results in artifacts due to block-based encoding and decoding. A common strategy to mitigate these artifacts is the adaptive loop filter (ALF) method, which calculates the optimal filter for each image frame. However, achieving global adaptive filtering increases bitrate during code transmission. To address this challenge, this paper introduces the global adaptive loop filter and machine learning-based model (GALFMLM), an algorithm that effectively eliminates artifacts and enhances the quality of reconstructed images. It employs a more rational pixel classification method, incorporates inherent sharpening and contrast enhancement effects, and trains global filter coefficients using a substantial amount of training data. Simultaneously, a rate-distortion optimization technique is used to determine whether adaptive or global filter coefficients should be employed during the encoding process. Notably, this approach leverages machine learning methods, offering computational speed advantages over deep learning-based techniques. Experimental results demonstrate significant improvements in image compression performance compared to the JPEG standard, highlighting the efficacy and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
Database: Academic Search Index
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DbLabel: Academic Search Index
An: 184747325
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1343.85217285156
IllustrationInfo
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  Data: Enhanced image compression with global adaptive loop filter and machine learning-based optimization.
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  Data: <searchLink fieldCode="AR" term="%22Hu%2C+Jianhua%22">Hu, Jianhua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Weimei%22">Wu, Weimei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 120760750@qq.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Bo%22">Wang, Bo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Jiahui%22">Yang, Jiahui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiaolin%22">Liu, Xiaolin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Guixiang%22">Luo, Guixiang</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Computational+Methods+in+Sciences+%26+Engineering+%28Sage+Publications+Inc%2E%29%22">Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.)</searchLink>. Jan2025, Vol. 25 Issue 1, p270-281. 12p.
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  Data: *<searchLink fieldCode="DE" term="%22JPEG+%28Image+coding+standard%29%22">JPEG (Image coding standard)</searchLink><br />*<searchLink fieldCode="DE" term="%22Adaptive+filters%22">Adaptive filters</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Contrast+effect%22">Contrast effect</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+compression%22">Image compression</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Lossy compression often results in artifacts due to block-based encoding and decoding. A common strategy to mitigate these artifacts is the adaptive loop filter (ALF) method, which calculates the optimal filter for each image frame. However, achieving global adaptive filtering increases bitrate during code transmission. To address this challenge, this paper introduces the global adaptive loop filter and machine learning-based model (GALFMLM), an algorithm that effectively eliminates artifacts and enhances the quality of reconstructed images. It employs a more rational pixel classification method, incorporates inherent sharpening and contrast enhancement effects, and trains global filter coefficients using a substantial amount of training data. Simultaneously, a rate-distortion optimization technique is used to determine whether adaptive or global filter coefficients should be employed during the encoding process. Notably, this approach leverages machine learning methods, offering computational speed advantages over deep learning-based techniques. Experimental results demonstrate significant improvements in image compression performance compared to the JPEG standard, highlighting the efficacy and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1177/14727978241303212
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 12
        StartPage: 270
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      – SubjectFull: JPEG (Image coding standard)
        Type: general
      – SubjectFull: Adaptive filters
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Contrast effect
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Image compression
        Type: general
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      – TitleFull: Enhanced image compression with global adaptive loop filter and machine learning-based optimization.
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            NameFull: Hu, Jianhua
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            NameFull: Wu, Weimei
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            NameFull: Wang, Bo
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            NameFull: Yang, Jiahui
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            NameFull: Liu, Xiaolin
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            – D: 01
              M: 01
              Text: Jan2025
              Type: published
              Y: 2025
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              Value: 25
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            – TitleFull: Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.)
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