Unified feature analysis in JPEG and JPEG 2000-compressed domains

K. M. Au, Ngai Fong Law, W. C. Siu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)


Retrieving images compressed by different algorithms typically involves a pre-processing operation to decompress them onto the spatial domain from which features are extracted for further analysis. Our objective is to investigate common features that can be found in JPEG-compressed and JPEG 2000-compressed images so that image indexing can be done directly in their respective compressed domains. A fundamental difference between JPEG and JPEG 2000 is their transforms; the former uses a block-based discrete cosine transform (BDCT) while the latter uses a wavelet transform (WT). Direct comparison on BDCT blocks and WT subbands cannot reveal their relationship. By employing our proposed subband-filtering model, the BDCT coefficients can be concatenated to form structures similar to WT subbands. Our theoretical studies show that the concatenated BDCT and WT filters share common characteristics in terms of passband regions, magnitude and energy spectra. In particular, their low-pass filters are identical for Haar wavelets and highly similar for other wavelet kernels. Despite the fact that compression can affect features that can be extracted, our experimental results confirm that common features can always be extracted from JPEG- and JPEG 2000-compressed domains irrespective of the values of the compression ratio and the types of WT kernels used. As a result, similar JPEG-compressed and JPEG 2000-compressed images can be retrieved from one another without requiring a full decompression.
Original languageEnglish
Pages (from-to)2049-2062
Number of pages14
JournalPattern Recognition
Issue number7
Publication statusPublished - 1 Jul 2007


  • Discrete cosine transform
  • Feature extraction
  • Image retrieval
  • JPEG
  • JPEG 2000
  • Significance map
  • Wavelet transform

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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