In this work we explore an overcomplete representation of
multiview imagery for the purpose of compression. We
present a rate-distortion (R-D) driven approach to decompose
multiview datasets into two additive parts which can
be interpreted as being the diffuse and specular components.
We apply different transforms to each component such that
the compressibility of input data is improved. We describe
a framework which performs the R-D optimized separation
in a registered domain to avoid the complexity of warping
between views. Experimental results highlight the benefits
of the proposed source separation approach in the context of
compression.