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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.