Self-learning-based post-processing for image/video deblocking via sparse representation

Chia Hung Yeh, Li Wei Kang*, Yi Wen Chiou, Chia Wen Lin, Shu Jhen Fan Jiang


研究成果: 雜誌貢獻期刊論文同行評審

38 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)891-903
期刊Journal of Visual Communication and Image Representation
出版狀態已發佈 - 2014 7月

ASJC Scopus subject areas

  • 訊號處理
  • 媒體技術
  • 電腦視覺和模式識別
  • 電氣與電子工程


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