Experimentally obtained thresholds for a conditional-replenishment image-sequence coder

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We present the results of several psychophysical experiments that we conducted to determine appropriate values for threshold parameters to be used for block classification in an image-sequence conditional-replenishment encoding algorithm. The algorithm partitions the image into square blocks of pixels and, following motion compensation, classifies them into two categories, “nonsmooth” and “nontextured” ones. Nontextured blocks are tested with criteria different from those used for testing nonsmooth blocks for deciding whether to replenish or to copy the block. Since the end product of such algorithms is judged by human observers, the objective of the experiments that we conducted was to obtain pertinent characteristics of the human visual system (HVS) for incorporating them into the threshold selection part of the encoding process. In three separate major experiments we obtained near-optimal values for three threshold parameters by testing the detectability of stationary as well as moving targets under various conditions. In applying our observations to conditional replenishment coding, target detectability will refer to the visibility of motion-compensated prediction error and a consequent need to replenish the corresponding part of the image. The three specific experiments involved (a) uniform targets against a uniform background of different intensity, (b) textured targets against a uniform background of the same intensity as the average target intensity, and (c) textured targets against a textured background with the same standard deviation but different average intensities. The experiments reveal how to adjust the values of the thresholds to suit local conditions and they give rise to an adaptive threshold modification technique, based on the HVS characteristics. This approach achieved significant reductions in the bit rate needed to encode image sequences, without affecting the perceived image quality.

Original languageEnglish (US)
Pages (from-to)79-91
Number of pages13
JournalJournal of Visual Communication and Image Representation
Issue number1
StatePublished - Mar 1993

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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