论文标题
图像压缩的高精度和低复杂性质量控制方法
A high accuracy and low complexity quality control method for image compression
论文作者
论文摘要
对于大规模静止图像编码任务，处理平台需要确保编码图像满足质量要求。因此，为图像编码产生自适应QP的质量控制算法具有重要的研究价值。但是，现有的质量控制方法受到低精度，过度计算成本或时间信息依赖性的限制。在本文中，我们提出了一个简洁的λ域线性失真模型和基于原始数据的准确模型参数估计方法。由于模型参数是从原始数据中获得的，因此提出的方法与RDO过程分解，并且可以应用于不同的图像编码器。实验表明，所提出的质量控制算法同时达到了文献中最高的控制精度和最低的延迟。阿里巴巴的电子商务平台的应用还表明，所提出的算法可以大大降低总比特率，同时大大降低了不良病例比率。
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 λ 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.
