Recent learning-based lossless image compression methods encode an image in the unit of subimages and achieve comparable performances to conventional non-learning algorithms. However, these methods do not consider the performance drop in the high-frequency region, giving equal consideration to the low and high-frequency areas. In this paper, we propose a new lossless image compression method that proceeds the encoding in a coarse-to-fine manner to separate and process low and high-frequency regions differently. We initially compress the low-frequency components and then use them as additional input for encoding the remaining high-frequency region. The low-frequency components act as a strong prior in this case, which leads to improved estimation in the high-frequency area. In addition, we design the frequency decomposition process to be adaptive to color channel, spatial location, and image characteristics. As a result, our method derives an image-specific optimal ratio of low/high-frequency components. Experiments show that the proposed method achieves state-of-the-art performance for benchmark high-resolution datasets.
翻译:最近以学习为基础的无损图像压缩方法在子图像单元中将图像编码成一个小图像单位,并实现与常规非学习算法的可比性能。 但是,这些方法并不考虑高频区域的性能下降,对低频和高频区域给予同等的考虑。在本文中,我们建议采用一种新的无损图像压缩方法,以粗略到松散的方式进行编码,以不同的方式将低频和高频区域分开和处理。我们最初压缩低频部分,然后将它们用作对其余高频区域进行编码的额外输入。低频部分是本案之前的一个强项,从而导致改进高频区域的估算。此外,我们设计频率分解过程是为了适应彩色通道、空间位置和图像特性。结果,我们的方法得出了低/高频区域图像特定的最佳比例。实验显示,拟议的方法在基准高分辨率数据集方面达到了最先进的性能。