Fast extraction of wavelet-based features from JPEG images for joint retrieval with JPEG2000 images

K. O. Cheng, Ngai Fong Law, W. C. Siu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


In this paper, some fast feature extraction algorithms are addressed for joint retrieval of images compressed in JPEG and JPEG2000 formats. In order to avoid full decoding, three fast algorithms that convert block-based discrete cosine transform (BDCT) into wavelet transform are developed, so that wavelet-based features can be extracted from JPEG images as in JPEG2000 images. The first algorithm exploits the similarity between the BDCT and the wavelet packet transform. For the second and third algorithms, the first algorithm or an existing algorithm known as multiresolution reordering is first applied to obtain bandpass subbands at fine scales and the lowpass subband. Then for the subbands at the coarse scale, a new filter bank structure is developed to reduce the mismatch in low frequency features. Compared with the extraction based on full decoding, there is more than 72% reduction in computational complexity. Retrieval experiments also show that the three proposed algorithms can achieve higher precision and recall than the multiresolution reordering, especially around the typical range of compression ratio.
Original languageEnglish
Pages (from-to)3314-3323
Number of pages10
JournalPattern Recognition
Issue number10
Publication statusPublished - 1 Oct 2010


  • Compressed-domain image retrieval
  • DCT
  • JPEG
  • JPEG2000
  • Wavelet

ASJC Scopus subject areas

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


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