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      • Department of Electrical and Electronics Engineering
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      Estimation of depth fields suitable for video compression based on 3-D structure and motion of objects

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      Author
      Alatan, A. A.
      Onural, L.
      Date
      1998-06
      Source Title
      IEEE Transactions on Image Processing
      Print ISSN
      1057-7149
      Publisher
      Institute of Electrical and Electronics Engineers
      Volume
      7
      Issue
      6
      Pages
      904 - 908
      Language
      English
      Type
      Article
      Item Usage Stats
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      96
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      Abstract
      Intensity prediction along motion trajectories removes temporal redundancy considerably in video compression algorithms. In three-dimensional (3-D) object-based video coding, both 3-D motion and depth values are required for temporal prediction. The required 3-D motion parameters for each object are found by the correspondence-based E-matrix method. The estimation of the correspondences - two-dimensional (2-D) motion field - between the frames and segmentation of the scene into objects are achieved simultaneously by minimizing a Gibbs energy. The depth field is estimated by jointly minimizing a defined distortion and bitrate criterion using the 3-D motion parameters. The resulting depth field is efficient in the rate-distortion sense. Bit-rate values corresponding to the lossless encoding of the resultant depth fields are obtained using predictive coding; prediction errors are encoded by a Lempel-Ziv algorithm. The results are satisfactory for real-life video scenes.
      Keywords
      3-D motion
      3-D structure
      Dense depth estimation
      Depth encoding
      Motion analysis
      Object-based video coding
      Rate-distortion theory
      Algorithms
      Gibbs free energy
      Image analysis
      Image coding
      Image quality
      Image segmentation
      Matrix algebra
      Object recognition
      Signal distortion
      Three dimensional computer graphics
      Dense depth estimation
      Rate distortion theory
      Image compression
      Permalink
      http://hdl.handle.net/11693/25351
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/83.679440
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      • Department of Electrical and Electronics Engineering 3524
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