# Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation

arXiv: https://arxiv.org/abs/2602.20097 · HTML: https://mlsanity.com/paper/2602.20097/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 69 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 14 of 14)

- [Stripformer: Strip Transformer for Fast Image Deblurring](https://mlsanity.com/paper/2204.04627/) (2022)
- [SUNet: Swin Transformer UNet for Image Denoising](https://mlsanity.com/paper/2202.14009/) (2022)
- [TTHRESH: Tensor Compression for Multidimensional Visual Data](https://mlsanity.com/paper/1806.05952/) (2018)
- [Deep Generative Adversarial Compression Artifact Removal](https://mlsanity.com/paper/1704.02518/) (2017)
- [SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors](https://mlsanity.com/paper/2111.02925/) (2021)
- [cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data](https://mlsanity.com/paper/2007.09625/) (2020)
- [FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs](https://mlsanity.com/paper/2304.12557/) (2023)
- [BlockCNN: A Deep Network for Artifact Removal and Image Compression](https://mlsanity.com/paper/1805.11091/) (2018)
- [MSz: An Efficient Parallel Algorithm for Correcting Morse-Smale Segmentations in Error-Bounded Lossy Compressors](https://mlsanity.com/paper/2406.09423/) (2024)
- [Time-varying Vector Field Compression with Preserved Critical Point Trajectories](https://mlsanity.com/paper/2510.25143/) (2025)
- [ROIBIN-SZ: Fast and Science-Preserving Compression for Serial Crystallography](https://mlsanity.com/paper/2206.11297/) (2022)
- [Optimizing Scientific Data Transfer on Globus with Error-bounded Lossy Compression](https://mlsanity.com/paper/2307.05416/) (2023)
- [A High-Quality Workflow for Multi-Resolution Scientific Data Reduction and Visualization](https://mlsanity.com/paper/2407.04267/) (2024)
- [TFZ: Topology-Preserving Compression of 2D Symmetric and Asymmetric Second-Order Tensor Fields](https://mlsanity.com/paper/2508.09235/) (2025)

## Abstract (arXiv)

Error-bounded lossy compression has been regarded as a promising way to address the ever-increasing amount of scientific data in today's high-performance computing systems. Pre-quantization, a critical technique to remove sequential dependency and enable high parallelism, is widely used to design and develop high-throughput error-controlled data compressors. Despite the extremely high throughput of pre-quantization based compressors, they generally suffer from low data quality with medium or large user-specified error bounds. In this paper, we investigate the artifacts generated by pre-quantization based compressors and propose a novel algorithm to mitigate them. Our contributions are fourfold: (1) We carefully characterize the artifacts in pre-quantization based compressors to understand the correlation between the quantization index and compression error; (2) We propose a novel quantization-aware interpolation algorithm to improve the decompressed data; (3) We parallelize our algorithm in both shared-memory and distributed-memory environments to obtain high performance; (4) We evaluate our algorithm and validate it with two leading pre-quantization based compressors using five real-world datasets. Experiments demonstrate that our artifact mitigation algorithm can effectively improve the quality of decompressed data produced by pre-quantization based compressors while maintaining their high compression throughput.