# Challenges and Solutions in Selecting Optimal Lossless Data Compression Algorithms

arXiv: https://arxiv.org/abs/2509.25219 · HTML: https://mlsanity.com/paper/2509.25219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 99 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets](https://mlsanity.com/paper/1707.08819/) (2017)
- [MADE: Masked Autoencoder for Distribution Estimation](https://mlsanity.com/paper/1502.03509/) (2015)
- [Real-Time Adaptive Image Compression](https://mlsanity.com/paper/1705.05823/) (2017)
- [Practical Full Resolution Learned Lossless Image Compression](https://mlsanity.com/paper/1811.12817/) (2018)
- [Integer Discrete Flows and Lossless Compression](https://mlsanity.com/paper/1905.07376/) (2019)
- [Learning Convolutional Networks for Content-weighted Image Compression](https://mlsanity.com/paper/1703.10553/) (2017)
- [Parallel Multiscale Autoregressive Density Estimation](https://mlsanity.com/paper/1703.03664/) (2017)
- [A Technical Overview of AV1](https://mlsanity.com/paper/2008.06091/) (2020)
- [Lossless Image Compression through Super-Resolution](https://mlsanity.com/paper/2004.02872/) (2020)
- [Learned Lossless Image Compression Through Interpolation With Low Complexity](https://mlsanity.com/paper/2212.13243/) (2022)
- [A Learned Pixel-by-Pixel Lossless Image Compression Method with 59K Parameters and Parallel Decoding](https://mlsanity.com/paper/2212.01185/) (2022)

## Abstract (arXiv)

The rapid growth of digital data has heightened the demand for efficient lossless compression methods. However, existing algorithms exhibit trade-offs: some achieve high compression ratios, others excel in encoding or decoding speed, and none consistently perform best across all dimensions. This mismatch complicates algorithm selection for applications where multiple performance metrics are simultaneously critical, such as medical imaging, which requires both compact storage and fast retrieval. To address this challenge, we present a mathematical framework that integrates compression ratio, encoding time, and decoding time into a unified performance score. The model normalizes and balances these metrics through a principled weighting scheme, enabling objective and fair comparisons among diverse algorithms. Extensive experiments on image and text datasets validate the approach, showing that it reliably identifies the most suitable compressor for different priority settings. Results also reveal that while modern learning-based codecs often provide superior compression ratios, classical algorithms remain advantageous when speed is paramount. The proposed framework offers a robust and adaptable decision-support tool for selecting optimal lossless data compression techniques, bridging theoretical measures with practical application needs.