Screen content images typically contain a mix of natural and synthetic image parts. Synthetic sections usually are comprised of uniformly colored areas and repeating colors and patterns. In the VVC standard, these properties are exploited using Intra Block Copy and Palette Mode. In this paper, we show that pixel-wise lossless coding can outperform lossy VVC coding in such areas. We propose an enhanced VVC coding approach for screen content images using the principle of soft context formation. First, the image is separated into two layers in a block-wise manner using a learning-based method with four block features. Synthetic image parts are coded losslessly using soft context formation, the rest with VVC.We modify the available soft context formation coder to incorporate information gained by the decoded VVC layer for improved coding efficiency. Using this approach, we achieve Bjontegaard-Delta-rate gains of 4.98% on the evaluated data sets compared to VVC.

翻译：屏幕内容图像通常包含自然图像部分与合成图像部分的混合。合成部分通常由均匀着色区域及重复颜色与图案构成。在VVC标准中，这些特性通过帧内块复制和调色板模式加以利用。本文证明在此类区域中，逐像素无损编码能够超越有损VVC编码的性能。我们提出一种基于软上下文生成原理的增强型VVC屏幕内容图像编码方法。首先，采用基于学习的四特征分块方法将图像划分为双层结构：合成图像部分通过软上下文生成进行无损编码，其余部分则采用VVC编码。我们改进现有软上下文生成编码器，使其融合已解码VVC层的信息以提升编码效率。实验表明，该方法在评估数据集上相比VVC实现了4.98%的Bjontegaard-Delta-rate增益。