Dictionary learning-based distributed compressive video sensing

Hung Wei Chen, Li Wei Kang, Chun Shien Lu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

44 Citations (Scopus)

Abstract

We address an important issue of fully low-cost and low-complex video compression for use in resource-extremely limited sensors/devices. Conventional motion estimation-based video compression or distributed video coding (DVC) techniques all rely on the high-cost mechanism, namely, sensing/sampling and compression are disjointedly performed, resulting in unnecessary consumption of resources. That is, most acquired raw video data will be discarded in the (possibly) complex compression stage. In this paper, we propose a dictionary learning-based distributed compressive video sensing (DCVS) framework to "directly" acquire compressed video data. Embedded in the compressive sensing (CS)-based single-pixel camera architecture, DCVS can compressively sense each video frame in a distributed manner. At DCVS decoder, video reconstruction can be formulated as an l1-minimization problem via solving the sparse coefficients with respect to some basis functions. We investigate adaptive dictionary/basis learning for each frame based on the training samples extracted from previous reconstructed neighboring frames and argue that much better basis can be obtained to represent the frame, compared to fixed basis-based representation and recent popular "CS-based DVC" approaches without relying on dictionary learning.

Original languageEnglish
Title of host publication28th Picture Coding Symposium, PCS 2010
Pages210-213
Number of pages4
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event28th Picture Coding Symposium, PCS 2010 - Nagoya, Japan
Duration: 2010 Dec 82010 Dec 10

Publication series

Name28th Picture Coding Symposium, PCS 2010

Conference

Conference28th Picture Coding Symposium, PCS 2010
Country/TerritoryJapan
CityNagoya
Period2010/12/082010/12/10

Keywords

  • Compressive sensing
  • Dictionary learning
  • L-minimization
  • Single-pixel camera
  • Sparse representation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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