2017
DOI: 10.1186/s13640-017-0164-7
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Prediction architecture based on block matching statistics for mixed spatial-resolution multi-view video coding

Abstract: The use of mixed spatial resolutions in multi-view video coding is a promising approach for coding videos efficiently at low bitrates. It can achieve a perceived quality, which is close to the view with the highest quality, according to the suppression theory of binocular vision. The aim of the work reported in this paper is to develop a new multi-view video coding technique suitable for low bitrate applications in terms of coding efficiency, computational and memory complexity, when coding videos, which conta… Show more

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Cited by 3 publications
(1 citation statement)
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“…Despite the incurred long delays and high memory consumption of AMVC-HBP, the technique delivers very little coding gain, as the adaptive prediction framework does not fully utilise interview and temporal correlations in less dense and slow moving sequences. A mixed-resolution multivew video coding technique, which uses the statistics of block matching to adaptively reorder the reference frames for multiview extension of the H.264/AVC, is proposed in [19]. This mixed-resolution multiview video codec has been improved by integrating the prediction architecture technique, which is developed in [17] using an adaptive reference frame reordering algorithm reported in [18].…”
Section: Introductionmentioning
confidence: 99%
“…Despite the incurred long delays and high memory consumption of AMVC-HBP, the technique delivers very little coding gain, as the adaptive prediction framework does not fully utilise interview and temporal correlations in less dense and slow moving sequences. A mixed-resolution multivew video coding technique, which uses the statistics of block matching to adaptively reorder the reference frames for multiview extension of the H.264/AVC, is proposed in [19]. This mixed-resolution multiview video codec has been improved by integrating the prediction architecture technique, which is developed in [17] using an adaptive reference frame reordering algorithm reported in [18].…”
Section: Introductionmentioning
confidence: 99%