Graph-based transform for data decorrelation

Junhui Hou, Hui Liu, Lap Pui Chau

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)


Transform coding can decorrelate data, and is widely used for data compression. The recent graph-based signal processing has been attracting an increasing amount of interest. In this paper, we investigate how to effectively explore the intercorrelation of a set of images as well as the spatial correlation of human motion capture data using graph-based transform (GT). Specifically, the graph structure (or matrix is first estimated by an optimization algorithm, and then the data is projected onto an orthogonal matrix consisting of eigenvectors of the estimated graph matrix, leading to sparse coefficients. Experimental results demonstrate that the GT-based method can decorrelate much better than DCT at an almost negligible price of overhead for the extremely sparse graph matrix.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Digital Signal Processing, DSP 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781509041657
Publication statusPublished - 2 Jul 2016
Externally publishedYes
Event2016 IEEE International Conference on Digital Signal Processing, DSP 2016 - Beijing, China
Duration: 16 Oct 201618 Oct 2016

Publication series

NameInternational Conference on Digital Signal Processing, DSP


Conference2016 IEEE International Conference on Digital Signal Processing, DSP 2016


  • Graph signal processing
  • motion capture
  • transform coding

ASJC Scopus subject areas

  • Signal Processing


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