Literature DB >> 19789112

Video coding focusing on block partitioning and occlusion.

Manoranjan Paul1, Manzur Murshed.   

Abstract

Among the existing block partitioning schemes, the pattern-based video coding (PVC) has already established its superiority at low bit-rate. Its innovative segmentation process with regular-shaped pattern templates is very fast as it avoids handling the exact shape of the moving objects. It also judiciously encodes the pattern-uncovered background segments capturing high level of interblock temporal redundancy without any motion compensation, which is favoured by the rate-distortion optimizer at low bit-rates. The existing PVC technique, however, uses a number of content-sensitive thresholds and thus setting them to any predefined values risks ignoring some of the macroblocks that would otherwise be encoded with patterns. Furthermore, occluded background can potentially degrade the performance of this technique. In this paper, a robust PVC scheme is proposed by removing all the content-sensitive thresholds, introducing a new similarity metric, considering multiple top-ranked patterns by the rate-distortion optimizer, and refining the Lagrangian multiplier of the H.264 standard for efficient embedding. A novel pattern-based residual encoding approach is also integrated to address the occlusion issue. Once embedded into the H.264 Baseline profile, the proposed PVC scheme improves the image quality perceptually significantly by at least 0.5 dB in low bit-rate video coding applications. A similar trend is observed for moderate to high bit-rate applications when the proposed scheme replaces the bi-directional predictive mode in the H.264 High profile.

Year:  2009        PMID: 19789112     DOI: 10.1109/TIP.2009.2033406

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  2 in total

1.  Fast Mode Decision in the HEVC Video Coding Standard by Exploiting Region with Dominated Motion and Saliency Features.

Authors:  Pallab Kanti Podder; Manoranjan Paul; Manzur Murshed
Journal:  PLoS One       Date:  2016-03-10       Impact factor: 3.240

2.  Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding.

Authors:  Manoranjan Paul; Rui Xiao; Junbin Gao; Terry Bossomaier
Journal:  PLoS One       Date:  2016-10-03       Impact factor: 3.240

  2 in total

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