Detection and removal of video defects using rational-based techniques

  • Authors:
  • Lazhar Khriji;Mahmoud Meribout;Moncef Gabbouj

  • Affiliations:
  • Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat, Oman;Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat, Oman;Institute of Signal Processing, Tampere University of Technology, FIN Tampere, Finland

  • Venue:
  • Advances in Engineering Software - Advanced algorithms and architectures for signal processing
  • Year:
  • 2005

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Abstract

This paper presents a Rational and Vector Rational based interpolator methods for reconstruction of missing data in video sequences. The interpolation of missing data is important in many areas of image processing, including the restoration of degraded motion pictures, reconstruction of dropouts in digital video and automatic re-touching of old photographs. Here, a detection technique is investigated for localization of the defects, and then a spatial vector rational interpolator algorithm is proposed to, reconstruct the missing data. This algorithm exhibits desirable properties, such as, edge and details preservation and accurate chromaticity estimation. In such approach, color image pixels are considered as three-component vectors in the color space that is more appropriate for the human visual system. Therefore, the inherent correlation that exists between the different color components is not ignored. This leads to better image quality compared to that obtained by component-wise or marginal processing. The experimental results demonstrate the usefulness of the vector rational interpolator in an application involving the restoration of defects in video sequences. The resulting edges obtained using the proposed interpolator are free from blockiness and jaggedness. The complexity evaluation of the algorithm shows that the implementation of the algorithm on a dedicated IMAP-based parallel hardware architecture can lead to an execution time of 5.7 and 15.6 ms for (256×256) binary and color images, respectively.