Multi-level pixel difference classification methods

Yin Chan, S. Y. Kung

Research output: Contribution to conferencePaper

13 Scopus citations

Abstract

Block matching motion estimation algorithms are useful in many video applications such as the block-based video coding scheme employed in MPEG 1/2 . A single-chip implementation of a motion estimator (ME) for high quality video compression domains has been the goal of many ongoing research projects. There are several complementary directions along which we can reduce hardware complexity, for example, (1) reduction of search points, and (2) simplification of criterion functions. The last category is what this paper focuses on. We study the algorithmic and architectural potentials of the Pixel Difference Classification (PDC) method and propose a generalization called Multi-level PDC (MPDC). The goal is to examine different hardware-complexity vs performance trade-offs. Moreover, we identify a subset of MPDC, the Bit-truncation (BT) method which has the most potential for hardware saving. Experimental results show that it offers attractive trade-offs. Under fixed bit rate constraints, it gives picture quality degradation of less than 0.5dB, which is non-perceivable, for up to 6-bit truncation. BT results in no complicated data or control flows. Hence the consequent hardware reduction is straightforward. The estimated overall encoder hardware saving ranges from 12% to 35% for 6-bit truncation.

Original languageEnglish (US)
Pages252-255
Number of pages4
StatePublished - Dec 1 1995
EventProceedings of the 1995 IEEE International Conference on Image Processing. Part 3 (of 3) - Washington, DC, USA
Duration: Oct 23 1995Oct 26 1995

Other

OtherProceedings of the 1995 IEEE International Conference on Image Processing. Part 3 (of 3)
CityWashington, DC, USA
Period10/23/9510/26/95

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Multi-level pixel difference classification methods'. Together they form a unique fingerprint.

  • Cite this

    Chan, Y., & Kung, S. Y. (1995). Multi-level pixel difference classification methods. 252-255. Paper presented at Proceedings of the 1995 IEEE International Conference on Image Processing. Part 3 (of 3), Washington, DC, USA, .