rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Efficient Predictive Algorithms for Image Compression
  name:
    name part: Rosário Lucas, Luís Filipe.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Barros da Silva, Eduardo Antônio.
    role:
      role term: author.
  name:
    name part: Maciel de Faria, Sérgio Manuel.
    role:
      role term: author.
  name:
    name part: Morais Rodrigues, Nuno Miguel.
    role:
      role term: author.
  name:
    name part: Liberal Pagliari, Carla.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIX, 169 p. 66 illus., 24 illus. in color. online resource.
  abstract: This book discusses efficient prediction techniques for the current state-of-the-art High Efficiency Video Coding (HEVC) standard, focusing on the compression of a wide range of video signals, such as 3D video, Light Fields and natural images. The authors begin with a review of the state-of-the-art predictive coding methods and compression technologies for both 2D and 3D multimedia contents, which provides a good starting point for new researchers in the field of image and video compression. New prediction techniques that go beyond the standardized compression technologies are then presented and discussed. In the context of 3D video, the authors describe a new predictive algorithm for the compression of depth maps, which combines intra-directional prediction, with flexible block partitioning and linear residue fitting. New approaches are described for the compression of Light Field and still images, which enforce sparsity constraints on linear models. The Locally Linear Embedding-based prediction method is investigated for compression of Light Field images based on the HEVC technology. A new linear prediction method using sparse constraints is also described, enabling improved coding performance of the HEVC standard, particularly for images with complex textures based on repeated structures. Finally, the authors present a new, generalized intra-prediction framework for the HEVC standard, which unifies the directional prediction methods used in the current video compression standards, with linear prediction methods using sparse constraints. Experimental results for the compression of natural images are provided, demonstrating the advantage of the unified prediction framework over the traditional directional prediction modes used in HEVC standard. Presents a state-of-the-art review of existing prediction technologies for compression of both 2D and 3D multimedia content; Discusses the most recent advances beyond the current, standardized technologies for image and vide…
  table of contents: Introduction -- Prediction techniques for image and video coding -- Image and video coding standards -- Compression of depth maps using predictive coding -- Sparse representation methods for image prediction -- Generalised optimal sparse predictors -- Conclusions and other research directions -- Test signals -- References.
  note: by Luís Filipe Rosário Lucas, Eduardo Antônio Barros da Silva, Sérgio Manuel Maciel de Faria, Nuno Miguel Morais Rodrigues, Carla Liberal Pagliari.
  subject:
    topic: Engineering
  subject:
    topic: Image processing
  subject:
    topic: Electronic circuits
  subject:
    topic: Engineering
  subject:
    topic: Circuits and Systems
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Image Processing and Computer Vision
  classification: TK7888.4
  classification: 621.3815
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319511801
  identifier: http://dx.doi.org/10.1007/978-3-319-51180-1
  record info:
    record creation date: 170209
    record change date: 20170209124352.0
    record identifier: 978-3-319-51180-1