1D representation of Isomap for united video coding

1D representation of Isomap for united video coding This paper proposes a 1D representation of isometric feature mapping (Isomap) based united video coding algorithms. First, 1D Isomap representations that maintain distances are generated which can achieve a very high compression ratio. Next, embedding and reconstruction algorithms for the 1D Isomap representation are presented that can transform samples from a high-dimensional space to a low-dimensional space and vice versa. Then, dictionary learning algorithms for training samples are proposed to compress the input samples. Finally, a unified coding framework for diverse videos based on a 1D Isomap representation is built. The proposed methods make full use of correlations between internal and external videos, which are not considered by classical methods. Simulation experiments have shown that the proposed methods can obtain higher peak signal-to-noise ratios than standard highly efficient video coding for similar bit per pixel levels in the low bit rate situation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Systems Springer Journals

1D representation of Isomap for united video coding

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Operating Systems; Data Storage Representation; Data Encryption; Computer Graphics
ISSN
0942-4962
eISSN
1432-1882
D.O.I.
10.1007/s00530-017-0551-z
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a 1D representation of isometric feature mapping (Isomap) based united video coding algorithms. First, 1D Isomap representations that maintain distances are generated which can achieve a very high compression ratio. Next, embedding and reconstruction algorithms for the 1D Isomap representation are presented that can transform samples from a high-dimensional space to a low-dimensional space and vice versa. Then, dictionary learning algorithms for training samples are proposed to compress the input samples. Finally, a unified coding framework for diverse videos based on a 1D Isomap representation is built. The proposed methods make full use of correlations between internal and external videos, which are not considered by classical methods. Simulation experiments have shown that the proposed methods can obtain higher peak signal-to-noise ratios than standard highly efficient video coding for similar bit per pixel levels in the low bit rate situation.

Journal

Multimedia SystemsSpringer Journals

Published: May 25, 2017

References

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