标题： STZ：科学数据的高质量高速流有损压缩框架 显示英文标题

标题： STZ: A High Quality and High Speed Streaming Lossy Compression Framework for Scientific Data

摘要： 误差有界有损压缩是减少科学数据体积最有效的解决方案之一。 对于有损压缩，渐进式解压和随机访问解压是关键特性，它们能够实现按需数据访问和灵活的分析流程。 然而，这些特性可能会严重降低压缩质量和速度。 为了解决这些限制，我们提出了一种新的流式压缩框架，该框架在保持高质量和高速度的同时，支持渐进式解压和随机访问解压。 我们的贡献有三点：(1) 我们设计了第一个同时支持渐进式解压和随机访问解压的压缩框架；(2) 我们引入了一种分层分区策略以支持这两种流式特性，并引入了一种分层预测机制，减轻了分区的影响并实现了高质量的压缩——甚至可以与最先进的非流式压缩器SZ3相媲美；(3) 我们的框架提供了高速的压缩和解压速度，比SZ3快多达6.7$\times$。 显示英文摘要

摘要： Error-bounded lossy compression is one of the most efficient solutions to reduce the volume of scientific data. For lossy compression, progressive decompression and random-access decompression are critical features that enable on-demand data access and flexible analysis workflows. However, these features can severely degrade compression quality and speed. To address these limitations, we propose a novel streaming compression framework that supports both progressive decompression and random-access decompression while maintaining high compression quality and speed. Our contributions are three-fold: (1) we design the first compression framework that simultaneously enables both progressive decompression and random-access decompression; (2) we introduce a hierarchical partitioning strategy to enable both streaming features, along with a hierarchical prediction mechanism that mitigates the impact of partitioning and achieves high compression quality -- even comparable to state-of-the-art (SOTA) non-streaming compressor SZ3; and (3) our framework delivers high compression and decompression speed, up to 6.7$\times$ faster than SZ3.