Enhanced stochastic bit reshuffling for fine granular scalable video coding

Wen-Hsiao Peng*, Tihao Chiang, Hsueh-Ming Hang, Chen-Yi Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose an enhanced stochastic bit reshuffling (SBR) scheme to deliver better subjective quality for fine granular scalable (FGS) video coding. Traditional bit-plane coding in FGS algorithm suffers from poor subjective quality due to zigzag and raster scanning order. To tackle this problem, our SBR rearranges the transmission order of each bit by its estimated rate-distortion performance. Particularly, we model the transform coefficient with a maximum like-lihood based Laplacian distribution and incorporate it into the context probability model for content-aware parameter estimation. Moreover, we use a dynamic priority management scheme for the SBR. Experimental results show that our enhanced SBR together with context adaptive binary arithmetic coding offers up to 1.5dB PSNR improvement and shows better visual quality as compared to the scheme in MPEG-4 FGS.

Keywords

  • Maximum Likelihood Estimator
  • Subjective Quality
  • Context Model
  • Uncertainty Interval
  • Context Adaptive Binary Arithmetic Code

Fingerprint Dive into the research topics of 'Enhanced stochastic bit reshuffling for fine granular scalable video coding'. Together they form a unique fingerprint.

Cite this