# Benchmarks and Visualization

## Quality

To evaluate the performance of our implementation we calculate the mean squared error on the unknown pixels of the benchmark images of [RRW+09].

## Visualization

The following videos show the iterates of the different methods. Note that the videos are timewarped.

 CF KNN LKM RW

## Performance

We compare the computational runtime of our solver with other solvers: pyAMG, UMFPAC, AMGCL, MUMPS, Eigen and SuperLU. Figure 3 shows that our implemented conjugate gradients method in combination with the incomplete Cholesky decomposition preconditioner outperforms the other methods by a large margin. For the iterative solver we used an absolute tolerance of $$10^{-7}$$, which we scaled with the number of known pixels, i.e. pixels that are either marked as foreground or background in the trimap.