Rate-distortion optimized distributed compressive video sensing

Jin Xu, Yuansong Qiao, Quan Wen

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Distributed compressive video sensing (DCVS) is an emerging low-complexity video coding framework which integrates the merits of distributed video coding (DVC) and compressive sensing (CS). In this paper, we propose a novel rate-distortion optimized DCVS codec, which takes advantage of a rate-distortion optimization (RDO) model based on the estimated correlation noise (CN) between a non-key frame and its side information (SI) to determine the optimal measurements allocation for the non-key frame. Because the actual CN can be more accurately recovered by our DCVS codec, it leads to more faithful reconstruction of the non-key frames by adding the recovered CN to the SI. The experimental results reveal that our DCVS codec significantly outperforms the legacy DCVS codecs in terms of both objective and subjective performance.

Original languageEnglish
Pages (from-to)1272-1276
Number of pages5
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE99A
Issue number6
DOIs
Publication statusPublished - Jun 2016

Keywords

  • Adaptive sparse recovery with SI
  • Distributed compressive video sensing
  • Rate-distortion optimized measurements allocation

Fingerprint

Dive into the research topics of 'Rate-distortion optimized distributed compressive video sensing'. Together they form a unique fingerprint.

Cite this