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What a useful compression report should measure
A final file size alone cannot tell you whether a compression option is suitable for an archive. A smaller file may take longer to produce, alter image content, affect searchability, or fail an applicable records requirement. Keep the measurements below together for each run.
- Input profile: Preserve the test set and record document class, input bytes, page count, and relevant text or image characteristics.
- Output size: Record output bytes, compression ratio (input bytes divided by output bytes), and percentage size reduction. Group results by document type and size band rather than relying only on an overall average.
- Processing performance: Record end-to-end elapsed time, per-document latency, documents or pages per second, concurrency, and resource use where available. Measure sustained throughput at the intended archive workload, not just the time to process one file.
- Pipeline configuration: Record the exact tool and version, runtime environment, PDF/A target if applicable, image resampling, color conversion, compression method, and any content removal.
- Preservation and compliance: Keep fidelity inspection, OCR/searchability checks where required, and validator or conformance results beside each size and performance result.
This is a practical test plan, not a published universal reporting standard; the cited sources do not establish a standard schema or a target throughput for commerce archives.
How to compare compression options fairly
Use the same preserved input sample for each option, then run each pipeline on the intended host and at the concurrency expected in production. Include the actual mix of records—such as born-digital invoices, scanned forms, or image-heavy statements only when those classes occur in your archive. Compare results by document class as well as across the collection, since content and conversion behavior influence both output and processing cost.
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- Establish the baseline: Run the current ingest path without changing settings and record its file sizes, end-to-end latency, sustained throughput, resource use, and validation results.
- Change one relevant setting or pipeline choice at a time: Examples include a different PDF/A target, lossless versus lossy image compression, or an image-resampling policy. Preserve the precise configuration for each run.
- Repeat under representative load: Measure per-document latency and sustained throughput at the target concurrency, rather than inferring archive performance from a single-file timing.
- Inspect preservation and compliance: Check visible content and imagery, OCR/searchability if required, and applicable validators or policy checks before accepting a smaller result.
- Report distributions and exceptions: Show results by document class and size band, and identify records that became larger, slower, or failed a preservation check instead of hiding them in a collection-wide average.
Evaluate each candidate across output size, ingest speed, fidelity and discoverability, compliance and portability, and operational cost. The Library of Congress describes the Archive Ingest and Handling Test as a practical test of transfer, ingest, management, and export between institutions; that workflow perspective supports assessing compression as part of the archive path rather than as an isolated file operation (Library of Congress: The Archive Ingest and Handling Test).
Why smaller output can carry trade-offs
Compression results depend on document contents and the conversion path. Lossless cleanup, such as removing duplicate resources, differs from lossy changes such as image downsampling or color conversion. Record whether image dimensions, color spaces, or visible content changed, and whether the chosen method is lossless or lossy. A smaller output is not automatically a better archival result.
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A PDF Association article reporting Big Faceless Organization conversion tests illustrates how version and implementation can affect size. Across its 15,083-PDF corpus, enabling object compression in PDF/A-3 was estimated to save 5.1% of the original archive size; removing object compression in PDF/A-1 was estimated to cost 0.6%. These are results for that corpus and implementation, not forecasts for another archive (PDF Association: Large Scale Testing and Analysis of PDF to PDF/A Conversion).
The same article gives a single 32-page example: Flate took 5 seconds to rasterize and 8.4 seconds to compress the raster pages to 10.5% of the original size. JPEG 2000 reduced that result by about half again, but compression took 29.1 seconds and page generation took about three times longer. This example demonstrates a possible size-versus-time trade-off; it is not a general performance benchmark.
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The article describes default Flate compression as lossless and fast, while DCT/JPEG is lossy and can introduce visible noise in line graphics or text. JPEG 2000 can be lossless or lossy, and the article notes it is unavailable for PDF/A-1. Datalogics’ vendor-authored comparison likewise reports variation across tools and inputs, and tests more than 20 tools and configurations; it should not be treated as an independent market ranking (Datalogics: PDF compression comparison).
Apply the records policy before choosing a setting
Compression limits depend on governing retention obligations and jurisdiction. NARA’s guidance concerns US federal digitized permanent paper records; it is not automatically binding on a private commerce archive or every other jurisdiction. For those federal records, NARA lists Deflate or lossless JPEG 2000 for PDF/A choices, and permits visually lossless JPEG 2000 up to 20:1 only after testing and visual evaluation for artifacts that obscure or alter information. NARA says it will not accept digitized PDFs saved with lossy compression to reduce file size, citing JPEG and JBIG2 as examples (NARA: Appendix A: Tables of File Formats).
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NARA also distinguishes embedded OCR from replacing the original image. Its guidance accepts embedded OCR when it does not substitute generated or modified content for the bitmapped original, and rejects OCR workflows that alter visible content, degrade the original image, or replace it with OCR text (NARA: Appendix A: Tables of File Formats). Commerce archives should define acceptance criteria from their own legal, contractual, and retention requirements, then validate outputs against those criteria.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use published benchmarks only as context
Vendor and corpus figures can help frame what to test, but they do not establish expected results for a different archive or pipeline. Nutrient’s benchmark page reports a peak sustained rendering result of 112 pages per second for a simple-document profile on a 16-vCPU reference server. Its benchmark coverage describes defined document profiles from 90 KB to 25 MB, mean and P95 API-operation latency, a 16-vCPU/32-GiB reference configuration, and PDF/A/PDF/UA validation comparisons. The figure is rendering performance, not a PDF compression or archive-ingest throughput guarantee; Nutrient notes that production results depend on document mix, hardware, network conditions, and application calls (Nutrient: Document Engine benchmarks).
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Before using vendor performance numbers for procurement, inspect the underlying reports and versions and compare their conditions with your intended workload. Your acceptance threshold should come from archive requirements and measured operational capacity, not from a published reference figure.
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