Interframe Coding of Digital Images Using Transform and Hybrid Transform/Predictive Techniques

Abstract

In the design of digital image coding systems, the principal objective is to achieve high quality receiver image reconstructions with a minimum number of transmitted code bits. Bit rate reductions are achieved by exploiting statistical redundancies within an image. This is combined by transmission of only those portions of the mathematical image representation which the human observer is most sensitive to. This dissertation describes research intended to extend current image coding techniques to the coding of sequences of digital images transmitted over a digital communications channel. The emphasis is directed towards definition of an image coding system that exploits temporal as well as spatial image redundancies. A primary objective of this investigation is to develop a class of interframe hybrid transform/ predictive coders having near optimum levels of performance. The interframe hybrid coder implementations considered employ two-dimensional unitary transforms in the spatial domain coupled with first-order DPCM predictive coding in the temporal domain. Based on a statistical image representation, a model is developed for the hybrid coder transform coefficient temporal difference variance matrix. With this model, theoretical MSE performance levels for the hybrid coder with zonal coding are determined as a function of spatial subblock size. Implementations of the interframe hybrid coder using discrete cosine and Fourier transforms are experimentally evaluated.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1976
Accession Number
ADA035083

Entities

People

  • John A. Roese

Organizations

  • University of Southern California

Tags

DTIC Thesaurus Topics

  • Coefficients
  • Digital Communications
  • Digital Images
  • Image Reconstruction
  • Images
  • Observers
  • Redundancy
  • Sequences
  • Theses
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Image Processing and Computer Vision.
  • Radio communications and signal processing.