A New Text Compression Algorithm Based on Index Permutation and Suffix Coding
Bibliographic record
Abstract
The rapid generation and utilization of text data, driven by the proliferation of the Internet of Things (IoT) and large language models, has intensified the need for efficient lossless text compression. To address this, we introduce HEES23, a novel lossless compression algorithm specifically designed for English text. HEES23 employs a unique suffix coding scheme incorporating new symbol representations and a fixed, language-optimized table to maximize compression efficiency. Additionally, the adaptive entropy reduction techniques combined with block sorting expose significant empirical entropy and redundancy in raw textual data. A key feature of HEES23 is its recursive mapping mechanism for index encoding and symbol extraction, which iteratively reduces redundancy while preserving data integrity. The algorithm has been experimentally applied to diverse human-generated text datasets and benchmarked against established standards. Results show that HEES23 achieves an average compression ratio exceeding 30% for data sizes as small as 0.1 kB, outperforming methods, such as Deflate, Brotli, LZ77, and bZIP2, which either result in negative compression or offer limited efficiency of around 10%. Furthermore, HEES23 maintains strong performance, achieving compression rates between 53% and 64% on larger and more complex datasets, underscoring its effectiveness for IoT applications requiring long-range, low-bandwidth communication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".