Dynamic Neural Network for Lossy-to-Lossless Image Coding

Tassnim Dardouri, Mounir Kaaniche, Amel Benazza-Benyahia, Jean-Christophe Pesquet

Abstract:

Lifting-based wavelet transform has been exten-
sively used for efficient compression of various types of visual
data. Generally, the performance of such coding schemes strongly
depends on the lifting operators used, namely the prediction and
update filters. Unlike conventional schemes based on linear filters,
we propose, in this paper, to learn these operators by exploiting
neural networks. More precisely, a classical Fully Connected
Neural Network (FCNN) architecture is firstly employed to
perform the prediction and update. Then, we propose to improve
this FCNN-based Lifting Scheme (LS) in order to better take into
account the input image to be encoded. Thus, a novel dynamical
FCNN model is developed, making the learning process adaptive
to the input image contents for which two adaptive learning
techniques are proposed. While the first one resorts to an iterative
algorithm where the computation of two kinds of variables
is performed in an alternating manner, the second learning
method aims to learn the model parameters directly through a
reformulation of the loss function. Experimental results carried
out on various test images show the benefits of the proposed
approaches in the context of lossy and lossless image compression.