As learned image codecs (LICs) become more prevalent, their low coding
efficiency for out-of-distribution data becomes a bottleneck for some
applications. To improve the performance of LICs for screen content (SC) images
without breaking backwards compatibility, we propose to introduce parameterized
and invertible linear transformations into the coding pipeline without changing
the underlying baseline codec's operation flow. We design two neural networks
to act as prefilters and postfilters in our setup to increase the coding
efficiency and help with the recovery from coding artifacts. Our end-to-end
trained solution achieves up to 10% bitrate savings on SC compression compared
to the baseline LICs while introducing only 1% extra parameters.

通过引入参数化和可逆的线性变换，我们的研究提出了一种方法来改善针对屏幕内容图像的学习图像编解码器（LICs）的编码效率，并在不改变基线编解码器操作流的情况下设计了两个神经网络作为预处理器和后处理器，从而帮助增加编码效率并减少编码的伪影。我们的端到端训练方法在屏幕内容图像压缩中相比基线的 LICs 实现了高达 10% 的比特率节省，同时只引入了额外的 1% 参数。