%0 Journal Article
	%A Navdeep Goel and  Salvador Gabarda
	%D 2016
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 109, 2016
	%T Optimal Image Representation for Linear Canonical Transform Multiplexing
	%U https://publications.waset.org/pdf/10003258
	%V 109
	%X Digital images are widely used in computer
applications. To store or transmit the uncompressed images
requires considerable storage capacity and transmission bandwidth.
Image compression is a means to perform transmission or storage of
visual data in the most economical way. This paper explains about
how images can be encoded to be transmitted in a multiplexing
time-frequency domain channel. Multiplexing involves packing
signals together whose representations are compact in the working
domain. In order to optimize transmission resources each 4 × 4
pixel block of the image is transformed by a suitable polynomial
approximation, into a minimal number of coefficients. Less than
4 × 4 coefficients in one block spares a significant amount of
transmitted information, but some information is lost. Different
approximations for image transformation have been evaluated as
polynomial representation (Vandermonde matrix), least squares +
gradient descent, 1-D Chebyshev polynomials, 2-D Chebyshev
polynomials or singular value decomposition (SVD). Results have
been compared in terms of nominal compression rate (NCR),
compression ratio (CR) and peak signal-to-noise ratio (PSNR)
in order to minimize the error function defined as the difference
between the original pixel gray levels and the approximated
polynomial output. Polynomial coefficients have been later encoded
and handled for generating chirps in a target rate of about two
chirps per 4 × 4 pixel block and then submitted to a transmission
multiplexing operation in the time-frequency domain.
	%P 40 - 46