通过多方向上下文建模，实现更高效的图像无损压缩。
Deep Lossless Image Compression via Masked Sampling and Coarse-to-Fine Auto-Regression
- 采用迭代掩码采样与粗到细自回归，融合多方向上下文信息。
- 在多个数据集上达到与现有方法相当的压缩率，编码速度更快。
- 适合需要高灵活性和感知一致性的图像压缩应用场景。
基于学习的无损图像压缩通常采用像素或子图自回归进行概率估计，性能良好。然而，现有方法仅考虑栅格顺序中当前符号之前的上下文依赖。我们认为应进一步考虑当前符号与未来符号之间的依赖关系。本文提出一种基于掩码采样与粗到细自回归的深度无损图像压缩方法，结合有损重建与渐进残差压缩，融合多方向上下文，更符合人类感知。具体地，残差通过 $T$ 次迭代掩码采样分解，每次包含三个步骤：概率估计、掩码计算和算术编码。迭代过程逐步优化预测，渐进还原真实图像。大量实验表明，相比传统与学习型无损压缩方法，本方法在多个数据集上达到相当的压缩性能，具备竞争力的编码速度与更高灵活性。
原文摘要 · Abstract (English)
Learning-based lossless image compression employs pixel-based or subimage-based auto-regression for probability estimation, which achieves desirable performances. However, the existing works only consider context dependencies in one direction, namely, those symbols that appear before the current symbol in raster order. We believe that the dependencies between the current and future symbols should be further considered. In this work, we propose a deep lossless image compression via masked sampling and coarse-to-fine auto-regression. It combines lossy reconstruction and progressive residual compression, which fuses contexts from various directions and is more consistent with human perception. Specifically, the residuals are decomposed via $T$ iterative masked sampling, and each sampling consists of three steps: 1) probability estimation, 2) mask computation, and 3) arithmetic coding. The iterative process progressively refines our prediction and gradually presents a real image. Extensive experimental results show that compared with the existing traditional and learned lossless compression, our method achieves comparable compression performance on extensive datasets with competitive coding speed and more flexibility.
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