Integrated classifier in classified coding

Jiwu Huang, Li Chen, Yun Q. Shi

Research output: Contribution to journalConference articlepeer-review

12 Scopus citations


Block coding is one of the most common schemes in image data compression. To improve the performance of block coding, a classification can be applied to the blocks prior to coding. This results in an adaptive block coding method, i.e, classified coding. In this paper, an integrated classifier taking the features of the human vision system (HVS) into account in classifier coding is proposed. First, we present a classifier based on the local contrast sensitivity of the HVS. Compared with the commonly used local variance-based classifier (LVC), it possesses lower computational complexity while preserving almost the same performance. To improve upon the single-parameter classifiers' poor adaptivity, we, then, propose an integrated classifier which is composed of three independent classification units. It exhibits the complementarity of different classifiers. The simulation results demonstrate that the proposed classifier performs better than the LVC.

Original languageEnglish (US)
Pages (from-to)146-149
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
StatePublished - 1998
EventProceedings of the 1998 IEEE International Symposium on Circuits and Systems, ISCAS. Part 5 (of 6) - Monterey, CA, USA
Duration: May 31 1998Jun 3 1998

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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