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场景与合成图像部分的混合。合成区域通常由均匀着色区域以及重复的颜色和图案构成。在VVC标准中，利用帧内块拷贝和调色板模式对这些特性进行了开发。本文表明，在这些区域中，逐像素无损编码的性能可超越有损VVC编码。我们提出一种基于软上下文形成原理的增强型VVC屏幕内容编码方法。首先，采用基于学习的四类块特征方法以块为单位将图像分为两层。合成图像部分通过软上下文形成进行无损编码，其余部分则采用VVC编码。我们对现有软上下文形成编码器进行改进，将解码后的VVC层所获信息融入其中，以提升编码效率。采用该方法，相较于VVC标准，我们在评估数据集上实现了4.98%的Bjontegaard-Delta率增益。