Multiple region-of-interest based H.264 encoder with a detection architecture in macroblock level pipelining

Tianruo Zhang, Chen Liu, Minghui Wang, Satoshi Goto

Research output: Contribution to journalArticle

Abstract

This paper proposes a region-of-interest (ROI) based H.264 encoder and the VLSI architecture of the ROI detection algorithm. In ROI based video coding system, pre-processing unit to detect ROI should only introduce low computational complexity overhead due to the low power requirement. The Macroblocks (MBs) in ROIs are detected sequentially in the same order of H.264 encoding to satisfy the MB level pipelining of ROI detector and H.264 encoder. ROI detection is performed in a novel estimation-and-verification process with an ROI contour template. Proposed architecture can be configured to detect either single ROI or multiple ROIs in each frame and the throughput of single detection mode is 5.5 times of multiple detection mode. 98.01% and 97.89% of MBs in ROIs can be detected in single and multiple detection modes respectively. Hardware cost of proposed architecture is only 4.68k gates. Detection speed is 753 fps for CIF format video at the operation frequency of 200 MHz in multiple detection mode with power consumption of 0.47mW. Compared with previous fast ROI detection algorithms for video coding application, the proposed architecture obtains more accurate and smaller ROI. Therefore, more efficient ROI based computation complexity and compression efficiency optimization can be implemented in H.264 encoder.

Original languageEnglish
Pages (from-to)401-410
Number of pages10
JournalIEICE Transactions on Electronics
VolumeE94-C
Issue number4
DOIs
Publication statusPublished - 2011 Apr

Keywords

  • H.264 encoding
  • Low power
  • Region-of-interest
  • VLSI architecture

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

Fingerprint Dive into the research topics of 'Multiple region-of-interest based H.264 encoder with a detection architecture in macroblock level pipelining'. Together they form a unique fingerprint.

  • Cite this