For large-scale still image coding tasks, the processing platform needs to ensure that the coded images meet the quality requirement. Therefore, the quality control algorithms that generate adaptive QP towards a target quality level for image coding are of significant research value. However, the existing quality control methods are limited by low accuracy, excessive computational cost, or temporal information dependence. In this paper, we propose a concise {\lambda} domain linear distortion model and an accurate model parameters estimation method based on the original data. Since the model parameters are obtained from the original data, the proposed method is decoupled from the RDO process and can be applied to different image encoders. Experiments show that the proposed quality control algorithm achieves the highest control accuracy and the lowest delay in the literature at the same time. The application of Alibaba's e-commerce platform also shows that the proposed algorithm can significantly reduce the overall bitrate while greatly reducing the bad case ratio.
翻译:对于大规模图像编码任务,处理平台需要确保编码图像符合质量要求。因此,产生适应性QP的质量控制算法,以达到图像编码的目标质量水平,具有重要的研究价值。然而,现有的质量控制方法受到低精度、过高计算成本或时间信息依赖的限制。在本文中,我们提出了一个简明的 ~lambda} 域线性扭曲模型和基于原始数据的准确模型参数估计方法。由于模型参数来自原始数据,因此,拟议方法与 RDO 进程脱钩,并可用于不同的图像编码器。实验显示,拟议的质量控制算法实现了最高控制准确性和在同一时间的文献中最短的延迟。应用 Alibaba 的电子商务平台还表明,拟议的算法可以大大减少总体比特率,同时大大降低不良案例比特率。