To judge whether PDF compression is safe for a high-throughput commerce archive, report three things together: output size, ingestion performance, and preservation or compliance results. A smaller file is not automatically a better archival file: compression may change visible content, searchability, or conformance, while PDF/A conversion can add processing time or even increase file size. There is no universal best setting; measure representative records with the intended software, configuration, and infrastructure.
What a useful PDF compression report should measure
Keep the test inputs and processing conditions fixed so that results can be compared fairly. For each run, record the source files, their characteristics, the exact conversion path, and both the output and operational results.
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- Input profile: document class, input bytes, page count, and relevant image and text characteristics. Use classes that actually occur in the archive, such as born-digital invoices, scanned forms, or image-heavy statements where applicable.
- Output size: output bytes, compression ratio (input bytes divided by output bytes), and percentage size reduction. Break results down by document type and size band rather than relying only on an archive-wide average.
- Processing performance: end-to-end latency per document, pages or documents per second, sustained throughput, and test concurrency. Measure the full ingest path, not just an isolated compression call.
- Preservation and discoverability: whether visible page content and source imagery remain intact, whether OCR and text searchability are preserved where required, and whether visual inspection found artifacts.
- Compliance and portability: the required PDF/A target or other records-transfer rules, validator results, and any dependency on particular codecs or workflows.
- Operational resource use: CPU time, memory, storage, and transfer costs where the target environment can measure them.
- Reproducibility details: tool and version, runtime environment, PDF/A target, image resampling, color conversion, content removal, and other relevant settings.
Keep a quality or conformance result beside every size result. An overall mean can conceal a damaging change in a small but important class of records, or a slowdown concentrated in large scanned files.
How to run a representative ingest test
Compression performance depends on the documents and the whole processing workflow. The Library of Congress describes its Archive Ingest and Handling Test as a practical test of transferring, ingesting, managing, and exporting a digital archive. That workflow-level perspective is useful here: evaluate compression as part of ingest and any required export, not as a stand-alone file operation.
#1 Best Overall
- Choose a representative sample. Include the archive’s actual document classes, page counts, file sizes, and image/text profiles. Do not assume that a sample of mostly born-digital PDFs predicts results for scanned forms.
- Define the policy checks first. Specify required preservation characteristics, PDF/A targets or other applicable conformance rules, OCR and searchability expectations, and acceptable visual changes before comparing settings.
- Fix and record the processing conditions. Identify the software and version, conversion route, settings, machine, and concurrency. Record options such as resampling, color conversion, content removal, and PDF/A version.
- Run the full pipeline at intended concurrency. Capture per-document latency and sustained pages- or documents-per-second results. Include ingest stages that could add time beyond compression itself.
- Validate the outputs. Check required conformance, visible content, imagery, and OCR or searchability. Record failures and artifacts by document class rather than treating them as one aggregate pass/fail number.
- Compare trade-offs by class. Look at output bytes, performance, quality, compliance, and resource use together. A setting that saves space on one class may add latency or reduce fidelity on another.
No universal reporting schema or target throughput for commerce archives is established by the available sources. This is a practical test plan, not a formal standard. Keep the inputs, environment, and settings with each run so later comparisons reflect real changes rather than an undocumented test setup.
How compression method and PDF/A conversion affect results
Compression options can produce different trade-offs in size, speed, and fidelity. A report should state what was done to the content, not just name the output format or show a smaller byte count.
Rank #2
Lossless cleanup and lossy changes
Lossless cleanup can reduce redundancy without intentionally discarding image information; Datalogics’ vendor-authored comparison describes removal of duplicate resources as one example. By contrast, image downsampling and color conversion can be lossy. The Datalogics comparison tested more than 20 tools and configurations, but it is vendor-authored and should not be read as an independent market ranking. Its broader point is important for reporting: results varied across inputs and tools, and the smallest output was not necessarily the best one.
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Flate, JPEG, and JPEG 2000
A PDF Association article reporting Big Faceless Organization’s conversion tests describes default Flate compression as lossless and fast. DCT/JPEG is lossy and can introduce visible noise in line graphics or text. JPEG 2000 may be lossless or lossy, so record which mode was used and inspect results rather than treating the codec name as proof of fidelity. The article also notes that JPEG 2000 is unavailable for PDF/A-1.
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PDF/A version and conversion overhead
PDF/A conversion can change both file size and processing cost. In a 2021 PDF Association article reporting Big Faceless Organization testing on a 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 implementation- and corpus-specific estimates, not predictions for another archive.
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 the result by about half again, but compression time increased to 29.1 seconds and page generation took about three times longer. This narrow example illustrates why size and latency belong in the same report; it is not a general benchmark.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Apply preservation requirements to the archive’s actual policy
Compression constraints depend on the records’ obligations and jurisdiction. The National Archives and Records Administration guidance applies to US federal records, not automatically to private commerce archives or archives governed by other jurisdictions. Its current online Appendix A: Tables of File Formats says digitized permanent paper records must use listed formats and codecs. For the listed PDF/A choices, it allows Deflate or lossless JPEG 2000. Visually lossless JPEG 2000 is permitted 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, giving JPEG and JBIG2 as examples.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11NARA also distinguishes embedded OCR from replacing the original image. Its OCR guidance accepts embedded OCR when it does not substitute generated or modified content for the bitmapped original; it rejects workflows that alter visible content, degrade the original image, or replace it with OCR text. A commerce archive should derive its acceptance checks from its own retention obligations rather than adopting federal guidance as a universal rule.
How to interpret published throughput figures
Published benchmarks help frame questions, but they do not establish production performance for a different document mix or ingest pipeline. Nutrient’s benchmark page describes API-operation mean and P95 latency, document profiles from 90 KB to 25 MB, PDF/A and PDF/UA validation comparisons, and throughput results on a 16-vCPU/32-GiB reference configuration. It reports peak sustained rendering of 112 pages per second for a simple-document profile. That is vendor-published rendering data, not a compression or archive-ingest throughput guarantee. Nutrient itself cautions: “Performance depends on your documents, hardware, network conditions, and how your application calls the SDK.”
Use such figures as context for what a vendor measured, not as a substitute for testing the target workload on target infrastructure. The same principle applies to corpus-specific conversion results and vendor-authored comparisons.
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