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020 _a9783319511801
_9978-3-319-51180-1
024 7 _a10.1007/978-3-319-51180-1
_2doi
050 4 _aTK7867-7867.5
072 7 _aTJFC
_2bicssc
072 7 _aTEC008010
_2bisacsh
072 7 _aTJFC
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082 0 4 _a621.3815
_223
100 1 _aRosário Lucas, Luís Filipe.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_960916
245 1 0 _aEfficient Predictive Algorithms for Image Compression
_h[electronic resource] /
_cby Luís Filipe Rosário Lucas, Eduardo Antônio Barros da Silva, Sérgio Manuel Maciel de Faria, Nuno Miguel Morais Rodrigues, Carla Liberal Pagliari.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXIX, 169 p. 66 illus., 24 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- 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.
520 _aThis 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 video compression, such as using the HEVC standard in the context of natural images, 3D and Light Field content; Includes new prediction methods based on alternative techniques and concepts, including flexible block partitioning, linear prediction, sparse representation.
650 0 _aElectronic circuits.
_919581
650 0 _aSignal processing.
_94052
650 0 _aComputer vision.
_960917
650 1 4 _aElectronic Circuits and Systems.
_960918
650 2 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aComputer Vision.
_960919
700 1 _aBarros da Silva, Eduardo Antônio.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_960920
700 1 _aMaciel de Faria, Sérgio Manuel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_960921
700 1 _aMorais Rodrigues, Nuno Miguel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_960922
700 1 _aLiberal Pagliari, Carla.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_960923
710 2 _aSpringerLink (Online service)
_960924
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319511795
776 0 8 _iPrinted edition:
_z9783319511818
776 0 8 _iPrinted edition:
_z9783319845883
856 4 0 _uhttps://doi.org/10.1007/978-3-319-51180-1
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80649
_d80649