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    National Tsing Hua University Institutional Repository > 電機資訊學院 > 電機工程學系 > 期刊論文 >  Self-learning-based post-processing for image/video deblocking via sparse representation


    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/83644


    Title: Self-learning-based post-processing for image/video deblocking via sparse representation
    Authors: Chia-Hung Yeh;Li-Wei Kang;Yi-Wen Chiou;Chia-Wen Lin;Shu-Jhen Fan Jiang
    教師: 林嘉文
    Date: 2014
    Publisher: Elsevier
    Relation: Journal of Visual Communication and Image Representation, Elsevier, Volume 25, Issue 5, July 2014, Pages 891-903
    Keywords: Blocking artifact
    Deblocking
    Sparse representation
    Dictionary learning
    Morphological component analysis
    Image/video restoration
    Image/video enhancement
    Post-processing
    Abstract: 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.
    Relation Link: http://www.elsevier.com/
    URI: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/83644
    Appears in Collections:[電機工程學系] 期刊論文
    [光電研究中心] 期刊論文

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