FaSST approximates sparse orthonormal transforms with mode-adaptive Givens rotation sequences to produce low-complexity secondary transforms for AV2 intra residuals that match LFNST rate-distortion performance at 83.67% lower complexity and deliver up to 1.80% BD-rate savings.
Convergence rates and source conditions for Tikhonov regularization with sparsity constraints
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abstract
This paper addresses the regularization by sparsity constraints by means of weighted $\ell^p$ penalties for $0\leq p\leq 2$. For $1\leq p\leq 2$ special attention is payed to convergence rates in norm and to source conditions. As main result it is proven that one gets a convergence rate in norm of $\sqrt{\delta}$ for $1\leq p\leq 2$ as soon as the unknown solution is sparse. The case $p=1$ needs a special technique where not only Bregman distances but also a so-called Bregman-Taylor distance has to be employed. For $p<1$ only preliminary results are shown. These results indicate that, different from $p\geq 1$, the regularizing properties depend on the interplay of the operator and the basis of sparsity. A counterexample for $p=0$ shows that regularization need not to happen.
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FaSST: Fast Sparsifying Secondary Transform
FaSST approximates sparse orthonormal transforms with mode-adaptive Givens rotation sequences to produce low-complexity secondary transforms for AV2 intra residuals that match LFNST rate-distortion performance at 83.67% lower complexity and deliver up to 1.80% BD-rate savings.