Image reconstruction from partial subband images and its application in packet video transmission

Yao Wang, V. Ramamoorthy

Research output: Contribution to journalArticlepeer-review


This paper addresses the problem of image reconstruction in a subband coding system when certain parts of one or several down-sampled sub-images are missing. By requiring that the sub-images produced from the reconstructed image be similar to those interpolated from the received sub-images, the loss recovery problem has been formulated as a quadratic optimization problem. Two reconstruction algorithms have been developed: a relaxational algorithm that achieves the optimal solution and a fast algorithm that leads to a sub-optimal solution. The interpolation scheme for the sub-images has been derived by characterizing each small image region by a texture or edge model. The proposed algorithms can be applied to any subband system and can accomodate various loss patterns. For the algorithm to work well, the analysis filters should have substantial overlap in their passbands such that the sub-images before down-sampling are correlated. Very good results have been obtained with some short kernel filter banks. The reconstructed image is satisfactory even when many parts of the low-low image is missing. It becomes unacceptable only if the lost regions contain certain periodic line structures which can cause Moiré patterns in the down-sampled sub-images. The results of our investigation suggest that signal loss problem such as packet loss can be combatted by using subband systems with overlapping filters. Although the coding efficiency is reduced compared to the conventional subband system using non-overlapping filters, the more disastrous signal loss can be prevented.

Original languageEnglish (US)
Pages (from-to)197-229
Number of pages33
JournalSignal Processing: Image Communication
Issue number2-3
StatePublished - Jun 1991


  • Image reconstruction from incomplete data
  • packet loss recovery
  • subband coding

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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