Lossless Compression Benchmark Results for High-Entropy Random Files

91.0 KB PDF document by alberto curiel, last updated April 2026. It presents results for a novel lossless compression system achieving reproducible reductions across multiple benchmark datasets, including high-entropy and structured data. All results are verified with 100% binary integrity.

Use Cases

• Benchmarking new compression algorithms based on the described reproducible reductions across multiple datasets.
• Testing theoretical limits of lossless compression based on results for files with entropy 8.
• Investigating novel information representation methods based on the described extension to Shannon's theory.

Strengths

• Results are verified with bit-perfect reconstruction (100% binary integrity).
• Compression system tested across multiple benchmark datasets, including high-entropy and structured data.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The 91.0 KB size suggests a limited scope, likely containing summary results rather than raw benchmark data.

Provenance

Source

alberto curiel via figshare

Collection Method

Likely contains experimental results from a proposed compression system.

Time Range

null

Freshness

Last updated 2026-04-09 19:15:42; freshness should be verified.

Geography

null