A framework for linear transform approximation using orthogonal basis projection

Yinpeng Chen, Hari Sundaram

Research output: Contribution to journalArticlepeer-review


This paper aims to develop a novel framework to systematically trade-off computational complexity with output distortion in linear multimedia transforms, in an optimal manner. The problem is important in real-time systems where the computational resources available are time-dependent. We solve the real-time adaptation problem by developing an approximate transform framework. There are three key contributions of this paper - (a) a fast basis projection approximation framework that allows us to store signal independent partial transform results to be used in real-time, (b) estimating the complexity distortion curve for the linear transform approximation using a given basis projection approximation set and searching for optimal transform approximation which satisfies the complexity constraint with minimum distortion and (c) determining optimal operating points on complexity distortion function and a meta-data embedding algorithm for images that allows for real-time adaptation. We have applied this approach on the FFT approximation for images with excellent results.

Original languageEnglish (US)
Pages (from-to)26-35
Number of pages10
JournalJournal of Multimedia
Issue number3
StatePublished - 2007
Externally publishedYes


  • Basis projection
  • Complexity distortion function
  • Linear transform approximation
  • Metadata encoding

ASJC Scopus subject areas

  • Media Technology
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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