A new efficient hybrid coding for progressive transmission of images

Ali N. Akansu, Richard A. Haddad

Research output: Contribution to journalArticlepeer-review

Abstract

The hybrid coding technique developed here involves a function of two concepts: Progressive interactive image transmission coupled with transform differential coding. There are two notable features in this approach. First, a local average of an mxm (typically 5 x 5) pixel array is formed, quantized and transmitted to the receiver for a preliminary display. This initial pass provides a crude but recognizable image before any further processing or encoding. Upon request from the receiver, the technique then switches to an iterative transform differential encoding scheme. Each iteration progressively provides more image detail at the receiver as requested. Secondly, this hybrid coding technique uses a computationally efficient, real, orthogonal transform, called the Modified Hermite Transform(MHT) [1], to encode the difference image. This MHT is then compared with the Discrete Cosine Transform(DCT) [2] for the same hybrid algorithm. For the standard images tested, we found that the progressive differential coding method per-forms comparably to the well-known direct transform coding methods. The DCT was used as the standard in this traditional approach. This hybrid technique was within 5% of SNR peak-to-peak for the "LENA" image. Comparisons between MHT and DCT as the transform vehicle for the hybrid technique were also conducted. For a transform block size N=8, the DCT requires 50% more multiplications than the MHT. The price paid for this efficiency is modest. For the example tested ("LENA"), the DCT performance gain was 4.2 dB while the MHT was 3.8 dB.

Original languageEnglish (US)
Pages (from-to)200-206
Number of pages7
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume1001
DOIs
StatePublished - Oct 25 1988

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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