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  • 1.
    Andersson, Kenneth
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Motion estimation for perceptual image sequence coding2003Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Since the advent of television obtaining high perceived quality using a limited bandwidth has been an important issue. This thesis proposes new methods for exploitation of temporal and perceptual redundancy in image sequences to achieve lower coding rate and/or higher visual quality. The methods presented are inspired and based on human visual system models. Particularly relevant in the present context are the indications that the visual cortex contains cells that are selective in orientation and frequency but invariant to the phase of the stimuli. For this reason a spatial quadrature filter bank, representing images in a similar fashion, is generated. For computational eciency, a filter net technique is employed using combinations of simple sequential 1D filter kernels. The lter bank is designed for interlaced video which is still the most common format for video sequences.

    For coding of image sequences temporal redundancy is reduced using motion compensated prediction. In motion compensated prediction the prediction of the next image is given by the present image and a predicted dense local motion field. Motion compensation is performed with a new and computationally ecient method. The method estimates data samples on a desired output grid from input data represented by samples on an irregularly grid. The initially predicted image is refined using forward motion compensation with a sparse motion field. In this case only the sparse motion field needs to be transmitted to the decoder. As a result a prediction without block artifacts, common in standard forward motion compensation schemes, is generated. Experiments show that this method performs better than traditional block-matching approaches.

    The motion is estimated using a new approach based on phase differences computed from products of quadrature filter responses. The approach includes learning parameters for motion estimation and introduces multiple hierarchical motion estimation to achieve estimates with high spatial resolution.

    The quadrature lter bank approach used for motion estimation also provides a basis for image quality estimation in accordance with human perception. This allows the video quality estimator to be an integral part of the video coder and opens up the possibility of local space-time optimization of video coding parameters.

  • 2.
    Andersson, Kenneth
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Quality and motion estimation for image sequence coding2002Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Since the advent of television obtaining high perceived quality using a limited bandwidth has been an important issue. This thesis proposes methods inspired and based on the human visual system for exploitation of temporal and perceptual redundancy in image sequences to achieve lower coding rate and/ or higher visual quality. Particularly relevant in the present context are the indications that the visual cortex contains cells that are selective in orientation and frequency but invariant to the phase of the stimuli. For this reason and for computational efficiency, a spatial quadrature filter bank is generated initially using combinations of simple sequential 1D filter kernels.

    The temporal redtmdancy is reduced using motion compensated prediction. Motion compensation predicts the frame to be transmitted by moving the previous decoded frame according to its motion, so called backward coding. This means that no information about motion needs to be transmitted to the decoder. The irregular sampling of the prediction, due to motion estimates with sub-pixel accuracy, is dealt with using a new method, continuous normalized convolution. Image values on an interlaced sampling grid are estimated from the irregularly sampled predictions using integration of signals and certainties over a neighbourhood employing a local model of both the signal and the integration filter. The motion is estimated from products of quadrature filter bank responses, generating phasebased channels tuned for velocity. This new approach includes learning parameters for motion estimation and introduces multiple hierarchical motion estimation toachieve estimates with high spatial resolution.

    Using the quadrature filter bank a new quality estimator, based on a human visual system model, is generated. The quality estimator can be used to reduce the perceptual redundancy of non-visible errors and is intended to improve coding performance in such regions.

  • 3.
    Andersson, Kenneth
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Andersson, Mats
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Johansson, Peter
    ISY LiTH.
    Forchheimer, Robert
    Linköping University, Department of Electrical Engineering.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Motion compensation using backward prediction and prediction refinement2003In: Signal Processing: Image Communication, ISSN 0923-5965, Vol. 18, no 5, p. 381-400Article in journal (Refereed)
    Abstract [en]

    This paper presents new methods for use of dense motion fields for motion compensation of interlaced video. The motion estimation is based on previously decoded field-images. The motion is then temporally predicted and used for motion compensated prediction of the field-image to be coded. The motion estimation algorithm is phase-based and uses two or three field-images to achieve motion estimates with sub-pixel accuracy. To handle non-constant motion and the specific characteristics of the field-image to be coded, the initially predicted image is refined using forward motion compensation, based on block-matching. Tests show that this approach achieves higher PSNR than forward block-based motion estimation, when coding the residual with the same coder. The subjective performance is also better.

  • 4.
    Andersson, Kenneth
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Andersson, Mats
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    A perception based velocity estimator and its use for motion compensated prediction2001In: SCIA 2001 Scandinavian Conference on Image Analysis,2001, 2001, p. 493-499Conference paper (Refereed)
    Abstract [en]

    The use of temporal redundancy is of vital importance for a successful video coding algorithm. An effective approach is the hybrid video coder where motion estimation is used for prediction of the next image frame and code the prediction error, and the motion field. The standard method for motion estimation is block matching as in MPEG-2, typically resulting in block artifacts. In this paper a perception based velocity estimator and its use for pixel based motion compensated prediction of interlaced video is presented.

  • 5.
    Andersson, Kenneth
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Johansson, Peter
    Linköping University, Department of Electrical Engineering, Image Coding. Linköping University, The Institute of Technology.
    Forchheimer, Robert
    Linköping University, Department of Electrical Engineering, Image Coding. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Backward-forward motion compensated prediction2002In: Proceedings of ACIVS 2002 (Advanced Concepts for Intelligent Vision Systems), Ghent, Belgium, September 9-11, 2002, 2002, p. 260-267Conference paper (Refereed)
    Abstract [en]

    This paper presents new methods for use of dense motion fields for motion compensation of interlaced video. The motion is estimated using previously decoded field-images. An initial motion compensated prediction is produced using the assumption that the motion is predictable in time. The motion estimation algorithm is phase-based and uses two or three field-images to achieve motion estimates with sub-pixel accuracy. To handle non-constant motion and the specific characteristics of the field-image to be coded, the initially predicted image is refined using forward motion compensation, based on block-matching. Tests show that this approach achieves higher PSNR than forward block-based motion estimation, when coding the residual with the same coder. The subjective performance is also better.

  • 6.
    Andersson, Kenneth
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Continuous normalized convolution2002In: Multimedia and Expo, 2002. ICME '02. Proceedings. 2002 IEEE International Conference on  (Volume:1), IEEE , 2002, p. 725-728Conference paper (Refereed)
    Abstract [en]

    The problem of signal estimation for sparsely and irregularly sampled signals is dealt with using continuous normalized convolution. Image values on real-valued positions are estimated using integration of signals and certainties over a neighbourhood employing a local model of both the signal and the used discrete filters. The result of the approach is that an output sample close to signals with high certainty is interpolated using a small neighbourhood. An output sample close to signals with low certainty is spatially predicted from signals in a large neighbourhood.

  • 7.
    Andersson, Kenneth
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Multiple hierarchical motion estimation2002In: Signal Processing, Pattern Recognition, and Applications - 2002 / [ed] M.H. Hamza, ACTA Press, 2002, p. 80-Conference paper (Refereed)
    Abstract [en]

    This paper introduce multiple hierarchical motion estimation to achieve motion estimates with high spatial resolution. The approach is based on phase-based motion estimation. Results show that the algorithm deal with the smooth motion field of hierarchical motion estimation while keeping the advantages of such an approach.

  • 8.
    Andersson, Kenneth
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    Laboratory of Mathematics in Imaging, Harvard Medical School, Brigham and Women's Hospital, Boston, USA.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Prediction from off-grid samples using continuous normalized convolution2007In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 87, no 3, p. 353-365Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel method for performing fast estimation of data samples on a desired output grid from samples on an irregularly sampled grid. The output signal is estimated using integration of signals over a neighbourhood employing a local model of the signal using discrete filters. The strength of the method is demonstrated in motion compensation examples by comparing to traditional techniques.

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