  In this work, we propose a two-stage video coding framework, as an extension
of our previous one-stage framework in [1]. The two-stage frameworks consists
two different dictionaries. Specifically, the first stage directly finds the
sparse representation of a block with a self-adaptive dictionary consisting of
all possible inter-prediction candidates by solving an L0-norm minimization
problem using an improved orthogonal matching pursuit with embedded
orthonormalization (eOMP) algorithm, and the second stage codes the residual
using DCT dictionary adaptively orthonormalized to the subspace spanned by the
first stage atoms. The transition of the first stage and the second stage is
determined based on both stages' quantization stepsizes and a threshold. We
further propose a complete context adaptive entropy coder to efficiently code
the locations and the coefficients of chosen first stage atoms. Simulation
results show that the proposed coder significantly improves the RD performance
over our previous one-stage coder. More importantly, the two-stage coder, using
a fixed block size and inter-prediction only, outperforms the H.264 coder
(x264) and is competitive with the HEVC reference coder (HM) over a large rate
range.
