Embedded singularity detection zerotree wavelet coding

Tai Chiu Hsung, Tommy C L Chan, Pak Kong Lun, David D. Feng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)


We explore the wavelet coefficient selection and denoising by singularity detection (SD) for the embedded zero-tree wavelet (EZW) coding algorithm in this paper. The EZW coding algorithm exploits the relation between the multi-scale wavelet coefficients that finer scale wavelet coefficients are probably to vanish if the coarse scale wavelet coefficient vanishes. It is true for the parts of image that are regular but not the cases for noise-like features. In other words, the performance of the coding algorithm may be greatly degraded for the latter. In this paper, we investigate to arrange the wavelet coefficients according to the local regularity, by using the computed wavelet coefficients from the encoding filters. The advantage is two folds. For normal coding, we can make the encoded bit-stream first appear with the wavelet coefficients that correspond to the most regular part of the image, and the irregular one's follows. For noisy image encoding, we can remove noises before encoding hence increase the image quality as well as the coding efficiency.
Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing
Number of pages5
Publication statusPublished - 1 Dec 1999
EventInternational Conference on Image Processing (ICIP'99) - Kobe, Japan
Duration: 24 Oct 199928 Oct 1999


ConferenceInternational Conference on Image Processing (ICIP'99)

ASJC Scopus subject areas

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


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