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Rate-Distortion Optimized Illumination Estimation for Wavelet-Based Video Coding

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.