We propose a rate-distortion optimized framework for estimating
illumination changes (lighting variations, fade in/out
effects) in a highly scalable coding system. Illumination
variations are realized using multiplicative factors in the image
domain and are estimated considering the coding cost
of the illumination field and input frames which are first
subject to a temporal Lifting-based Illumination Adaptive
Transform (LIAT). The coding cost is modelled by an L1-
norm optimization problem which is derived to approximate
a quadratic-log function which emerges from rate-distortion
considerations. The optimization problem is solved using
ADMM. The proposed solution works the same or better
than a mesh-based approach proposed in prior work, where
sparsity was controlled by explicitly choosing mesh parameters.
In the compression-inspired formulation presented here,
sparsity is discovered automatically through the solution of a
convex program that depends only on a target rate-distortion
operating point.