ISSN : 1796-2048
Volume : 2    Issue : 3    Date : June 2007

A Framework for Linear Transform Approximation Using Orthogonal Basis Projection
Yinpeng Chen and Hari Sundaram
Page(s): 26-35
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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

Index Terms
linear transform approximation, basis projection, complexity distortion function, metadata encoding