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  • 1.
    Li, Yun
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems.
    Sjöström, Mårten
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems.
    Olsson, Roger
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems.
    Jennehag, Ulf
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems.
    Scalable coding of plenoptic images by using a sparse set and disparities2016In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 25, no 1, p. 80-91, article id 7321029Article in journal (Refereed)
    Abstract [en]

    One of the light field capturing techniques is the focused plenoptic capturing. By placing a microlens array in front of the photosensor, the focused plenoptic cameras capture both spatial and angular information of a scene in each microlens image and across microlens images. The capturing results in significant amount of redundant information, and the captured image is usually of a large resolution. A coding scheme that removes the redundancy before coding can be of advantage for efficient compression, transmission and rendering. In this paper, we propose a lossy coding scheme to efficiently represent plenoptic images. The format contains a sparse image set and its associated disparities. The reconstruction is performed by disparity-based interpolation and inpainting, and the reconstructed image is later employed as a prediction reference for the coding of the full plenoptic image. As an outcome of the representation, the proposed scheme inherits a scalable structure with three layers.The results show that plenoptic images are compressed efficiently with over 60 percent bit rate reduction compared to HEVC intra, and with over 20 percent compared to HEVC block copying mode.

  • 2.
    Schwarz, Sebastian
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems.
    Sjöström, Mårten
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems.
    Olsson, Roger
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems.
    A Weighted Optimization Approach to Time-of-Flight Sensor Fusion2014In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 23, no 1, p. 214-225Article in journal (Refereed)
    Abstract [en]

    Acquiring scenery depth is a fundamental task in computer vision, with many applications in manufacturing, surveillance, or robotics relying on accurate scenery information. Time-of-flight cameras can provide depth information in real-time and overcome short-comings of traditional stereo analysis. However, they provide limited spatial resolution and sophisticated upscaling algorithms are sought after. In this paper, we present a sensor fusion approach to time-of-flight super resolution, based on the combination of depth and texture sources. Unlike other texture guided approaches, we interpret the depth upscaling process as a weighted energy optimization problem. Three different weights are introduced, employing different available sensor data. The individual weights address object boundaries in depth, depth sensor noise, and temporal consistency. Applied in consecutive order, they form three weighting strategies for time-of-flight super resolution. Objective evaluations show advantages in depth accuracy and for depth image based rendering compared with state-of-the-art depth upscaling. Subjective view synthesis evaluation shows a significant increase in viewer preference by a factor of four in stereoscopic viewing conditions. To the best of our knowledge, this is the first extensive subjective test performed on time-of-flight depth upscaling. Objective and subjective results proof the suitability of our approach to time-of-flight super resolution approach for depth scenery capture.

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