Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images
Kusetogullari, Huseyin and Yavariabdi, Amir
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Abstract
In this paper, a novel method for generation of multiple description
(MD) wavelet based image coding is proposed by using Multi-Objective
Evolutionary Algorithms (MOEAs). Complexity of the multimedia
transmission problem has been increased for MD coders if an input image
is affected by any type of noise. In this case, it is necessary to solve
two different problems which are designing the optimal side quantizers
and estimating optimal parameters of the denoising filter. Existing MD
coding (MDC) generation methods are capable of solving only one problem
which is to design side quantizers from the given noise-free image but
they can fail reducing any type of noise on the descriptions if they
applied to the given noisy image and this will cause bad quality of
multimedia transmission in networks. Proposed method is used to overcome
these difficulties to provide effective multimedia transmission in lossy
networks. To achieve it, Dual Tree-Complex Wavelet Transform (DT-CWT) is
first applied to the noisy image to obtain the subbands or set of
coefficients which are used as a search space in the optimization
problem. After that, two different objective functions are
simultaneously employed in the MOEA to find pareto optimal solutions
with the minimum costs by evolving the initial individuals through
generations. Thus, optimal quantizers are created for MDCs generation
and obtained optimum parameters are used in the image filter to remove
the mixed Gaussian impulse noise on the descriptions effectively. The
results demonstrate that proposed method is robust to the mixed Gaussian
impulse noise, and offers a significant improvement of optimal side
quantizers for balanced MDCs generation at different bitrates. (C) 2018
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