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.

翻译：随着学得图像编解码器（LICs）的日益普及，其对分布外数据较低编码效率已成为某些应用场景的瓶颈。为在不破坏后向兼容性的前提下，提升LICs对屏幕内容（SC）图像的编码性能，我们提出在编码流水线中引入参数化可逆线性变换，且不改变底层基础编解码器的操作流程。我们设计两个神经网络分别作为预处理滤波器与后处理滤波器，以提升编码效率并辅助修复编码伪影。经端到端训练后，本方案在SC压缩任务中较基线LICs可节省高达10%码率，同时仅引入1%额外参数量。