面向无损屏幕内容压缩的增强调色板建模
Enhanced Color Palette Modeling for Lossless Screen Content Compression
AI总结:
本研究针对屏幕内容无损编码的软上下文构建方法,提出利用前序阶段信息去冗余、省略隐式阶段决策信号传输的调色板增强方案,有效降低了比特率,性能优于VVC和HEVC。
摘要:
软上下文构建是一种针对屏幕内容的无损图像编码方法,它通过收集统计信息进行概率分布估计,再借助算术编码逐像素对图像编码。其主要流程包含三个阶段,分别是基于上下文模型的阶段、调色板阶段和残差编码阶段;由于必要的统计信息(如颜色或上下文)尚未习得,仅在前一阶段无法应用时才会启用后一阶段。我们提出了以下增强方案:首先,利用前序阶段的信息去除后续阶段中冗余的调色板条目与预测误差;此外,隐式已知的阶段决策信号不再进行显式传输。这些改进使得在评估数据上的平均比特率降低了1.07%。针对24位屏幕内容图像,所提方法的平均每像素比特数较VVC与HEVC分别少约0.44和0.17。
英文摘要:
Soft context formation is a lossless image coding method for screen content. It encodes images pixel by pixel via arithmetic coding by collecting statistics for probability distribution estimation. Its main pipeline includes three stages, namely a context model based stage, a color palette stage and a residual coding stage. Each subsequent stage is only employed if the previous stage can not be applied since necessary statistics, e.g. colors or contexts, have not been learned yet. We propose the following enhancements: First, information from previous stages is used to remove redundant color palette entries and prediction errors in subsequent stages. Additionally, implicitly known stage decision signals are no longer explicitly transmitted. These enhancements lead to an average bit rate decrease of 1.07% on the evaluated data. Compared to VVC and HEVC, the proposed method needs roughly 0.44 and 0.17 bits per pixel less on average for 24-bit screen content images, respectively.
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