Self-learning-based post-processing for image/video deblocking via sparse representation
•First MCA self-learning-based image decomposition framework.•Fully automatic and self-contained learning, no extra training samples required.•Highly adaptable to any block transform-based compressed image/video.•Adaptable with no info on data source, coding bitrates, and compression algorithms.•Ach...
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| Veröffentlicht in: | Journal of visual communication and image representation Jg. 25; H. 5; S. 891 - 903 |
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01.07.2014
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| Abstract | •First MCA self-learning-based image decomposition framework.•Fully automatic and self-contained learning, no extra training samples required.•Highly adaptable to any block transform-based compressed image/video.•Adaptable with no info on data source, coding bitrates, and compression algorithms.•Achieves better performance than existing deblocking algorithms.
Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a common problem in block-based image/video compression, especially at low bitrate coding. Various post-processing techniques have been proposed to reduce blocking artifacts, but they usually introduce excessive blurring or ringing effects. This paper proposes a self-learning-based post-processing framework for image/video deblocking by properly formulating deblocking as an MCA (morphological component analysis)-based image decomposition problem via sparse representation. Without the need of any prior knowledge (e.g., the positions where blocking artifacts occur, the algorithm used for compression, or the characteristics of image to be processed) about the blocking artifacts to be removed, the proposed framework can automatically learn two dictionaries for decomposing an input decoded image into its “blocking component” and “non-blocking component.” More specifically, the proposed method first decomposes a frame into the low-frequency and high-frequency parts by applying BM3D (block-matching and 3D filtering) algorithm. The high-frequency part is then decomposed into a blocking component and a non-blocking component by performing dictionary learning and sparse coding based on MCA. As a result, the blocking component can be removed from the image/video frame successfully while preserving most original visual details. Experimental results demonstrate the efficacy of the proposed algorithm. |
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| AbstractList | Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a common problem in block-based image/video compression, especially at low bitrate coding. Various post-processing techniques have been proposed to reduce blocking artifacts, but they usually introduce excessive blurring or ringing effects. This paper proposes a self-learning-based post-processing framework for image/video deblocking by properly formulating deblocking as an MCA (morphological component analysis)-based image decomposition problem via sparse representation. Without the need of any prior knowledge (e.g., the positions where blocking artifacts occur, the algorithm used for compression, or the characteristics of image to be processed) about the blocking artifacts to be removed, the proposed framework can automatically learn two dictionaries for decomposing an input decoded image into its "blocking component" and "non-blocking component." More specifically, the proposed method first decomposes a frame into the low-frequency and high-frequency parts by applying BM3D (block-matching and 3D filtering) algorithm. The high-frequency part is then decomposed into a blocking component and a non-blocking component by performing dictionary learning and sparse coding based on MCA. As a result, the blocking component can be removed from the image/video frame successfully while preserving most original visual details. Experimental results demonstrate the efficacy of the proposed algorithm. •First MCA self-learning-based image decomposition framework.•Fully automatic and self-contained learning, no extra training samples required.•Highly adaptable to any block transform-based compressed image/video.•Adaptable with no info on data source, coding bitrates, and compression algorithms.•Achieves better performance than existing deblocking algorithms. Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a common problem in block-based image/video compression, especially at low bitrate coding. Various post-processing techniques have been proposed to reduce blocking artifacts, but they usually introduce excessive blurring or ringing effects. This paper proposes a self-learning-based post-processing framework for image/video deblocking by properly formulating deblocking as an MCA (morphological component analysis)-based image decomposition problem via sparse representation. Without the need of any prior knowledge (e.g., the positions where blocking artifacts occur, the algorithm used for compression, or the characteristics of image to be processed) about the blocking artifacts to be removed, the proposed framework can automatically learn two dictionaries for decomposing an input decoded image into its “blocking component” and “non-blocking component.” More specifically, the proposed method first decomposes a frame into the low-frequency and high-frequency parts by applying BM3D (block-matching and 3D filtering) algorithm. The high-frequency part is then decomposed into a blocking component and a non-blocking component by performing dictionary learning and sparse coding based on MCA. As a result, the blocking component can be removed from the image/video frame successfully while preserving most original visual details. Experimental results demonstrate the efficacy of the proposed algorithm. |
| Author | Lin, Chia-Wen Chiou, Yi-Wen Fan Jiang, Shu-Jhen Kang, Li-Wei Yeh, Chia-Hung |
| Author_xml | – sequence: 1 givenname: Chia-Hung surname: Yeh fullname: Yeh, Chia-Hung organization: Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan – sequence: 2 givenname: Li-Wei surname: Kang fullname: Kang, Li-Wei email: lwkang@yuntech.edu.tw organization: Graduate School of Engineering Science and Technology-Doctoral Program, National Yunlin University of Science and Technology, Yunlin 640, Taiwan – sequence: 3 givenname: Yi-Wen surname: Chiou fullname: Chiou, Yi-Wen organization: Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan – sequence: 4 givenname: Chia-Wen surname: Lin fullname: Lin, Chia-Wen organization: Department of Electrical Engineering, National Tsing Hua University, Hsinchu 300, Taiwan – sequence: 5 givenname: Shu-Jhen surname: Fan Jiang fullname: Fan Jiang, Shu-Jhen organization: Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan |
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| Keywords | Image/video restoration Blocking artifact Deblocking Post-processing Image/video enhancement Sparse representation Morphological component analysis Dictionary learning Block code Dictionaries Image processing Noise reduction Data compression Video signal Multivariate analysis Low frequency Block matching Correspondence analysis Morphological analysis Self learning Computer vision Filtering blurring image Blocking Image compression Software component Sparse coding Noisy image Image analysis Experimental result Boundary value problem Scene analysis High frequency Image enhancement |
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| SubjectTerms | Algorithms Applied sciences Artificial intelligence Blocking Blocking artifact Coding Coding, codes Computer science; control theory; systems Deblocking Decomposition Detection, estimation, filtering, equalization, prediction Dictionary learning Exact sciences and technology Image/video enhancement Image/video restoration Information, signal and communications theory Morphological component analysis Pattern recognition. Digital image processing. Computational geometry Post-processing Representations Signal and communications theory Signal, noise Sparse representation Telecommunications and information theory |
| Title | Self-learning-based post-processing for image/video deblocking via sparse representation |
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