The Complete Guide to File Compression: PDF, Image, Video & Document Compression Explained

Uglypear Data — Fri, 24 Jul 2026 07:54:55 +0000

The Complete Guide to File Compression: PDF, Image, Video & Document Compression Explained

File compression is one of the most common needs in everyday work. An email attachment exceeding 25MB won't send, a PDF is too large to upload to a system, a folder of high-res photos is eating up your disk space — these scenarios all point to the same problem: the file needs to be smaller.

But "compression" means very different things in different contexts. Zipping files with 7-Zip is compression. Shrinking a 5MB photo to 500KB is also compression. Converting a 4K video to 720p? That's compression too. The technical principles behind each are fundamentally different, and so are their use cases.

This guide covers the core principles of file compression and walks through PDF compression, image compression, video compression, OFD compression, and enterprise data compliance — helping you understand the logic behind each file type and choose the right approach.

1. Fundamentals of File Compression

At its core, file compression is about representing the same information with less data. Based on whether information is lost, compression falls into two categories:

graph TD
    A[File Compression] --> B[Lossless]
    A --> C[Lossy]

    B --> B1[DEFLATE<br/>ZIP/gzip]
    B --> B2[LZMA/LZMA2<br/>7z format]
    B --> B3[PNG Filter + DEFLATE]

    C --> C1[DCT<br/>JPEG core]
    C --> C2[Quantization<br/>coefficient rounding]
    C --> C3[Inter-frame Prediction<br/>H.264/H.265]

    B1 --> B1a[Text/Code/Archives]
    B2 --> B2a[High-ratio Archives]
    B3 --> B3a[Lossless Images]

    C1 --> C1a[JPEG Images]
    C2 --> C2a[MP3 Audio]
    C3 --> C3a[MP4 Video]

    style B fill:#4CAF50,color:#fff
    style C fill:#FF9800,color:#fff

1.1 Lossless Compression

Lossless compression reduces file size while ensuring the decompressed data is byte-for-byte identical to the original. It works by eliminating redundancy — for example, a sequence like AAAAABBBCC can be encoded as 5A3B2C, instantly halving the storage space.

Common algorithms include:

DEFLATE: The core algorithm used by ZIP/gzip, combining LZ77 + Huffman coding
LZMA/LZMA2: Used by 7-Zip's .7z format, offering higher compression ratios than DEFLATE
PNG filtering + DEFLATE: Applies row-level filtering to image data before DEFLATE compression

Typical applications: ZIP/7z archive compression, PNG images, FLAC audio. Lossless compression is ideal for text, code, spreadsheets — anything where losing even a single bit is unacceptable.

1.2 Lossy Compression

Lossy compression achieves smaller sizes by discarding information that human eyes or ears are less sensitive to. For example, a 4000×3000 photo displayed on a 1920×1080 screen doesn't need all those extra pixels — they can be safely discarded.

Common techniques include:

DCT (Discrete Cosine Transform): The core of JPEG, converting images from the spatial domain to the frequency domain and discarding high-frequency components
Quantization: Rounding frequency coefficients at a given precision — lower precision means smaller files but more loss
Inter-frame prediction: Used in video compression (H.264/H.265) to leverage similarity between consecutive frames, encoding only the differences

Typical applications: JPEG images, MP3 audio, MP4/H.264 video. Lossy compression is suited for images, video, audio — media where some quality loss is acceptable.

Understanding the distinction between these two approaches is the foundation for choosing compression tools and parameters. Most document compression (PDF, Word) uses both methods simultaneously — lossless for text, lossy for embedded images and video.

2. Archive Compression vs. Content Compression: A Critical Distinction

Many developers confuse two fundamentally different compression needs:

graph LR
    subgraph Archive Compression
        A1[File A 30MB] --> R1[Package]
        A2[File B 20MB] --> R1
        A3[File C 10MB] --> R1
        R1 --> O1[archive.zip<br/>59MB<br/>only 1% reduction]
        O1 --> N1[After extraction<br/>original sizes restored]
    end

    subgraph Content Compression
        B1[PDF file 50MB] --> R2[Content optimization]
        R2 --> O2[compressed.pdf<br/>8MB<br/>84% reduction]
        O2 --> N2[Open directly<br/>no extraction needed]
    end

    style R1 fill:#2196F3,color:#fff
    style R2 fill:#4CAF50,color:#fff
    style O1 fill:#FFC107
    style O2 fill:#8BC34A,color:#fff

Dimension	Archive Compression (7-Zip/WinRAR)	Content Compression (Specialized Tools)
What it does	Packages files into .zip/.7z/.rar	Directly reduces individual file size
Target	Collections of any files	PDF, images, video, Office documents
Method	Lossless (redundancy elimination)	Lossy + lossless hybrid (content optimization)
Usage	Must extract before use	Open directly after compression
Typical scenario	Bundling multiple files for transfer	Reducing individual file size

The bottom line: if your problem is "this file is too big to email," you need content compression, not archive compression — because when the recipient extracts the .zip, the file inside is still the same size.

# Archive compression: file size unchanged, just packaged
Original 50MB PDF → 7z archive → 49MB (only 2% reduction)

# Content compression: directly reduces the file itself
Original 50MB PDF → content compression → 8MB (84% reduction)

3. PDF Compression

PDF is the most common file type that needs compression in office environments. Contracts, reports, presentations, electronic invoices — vast numbers of documents circulate as PDFs, and embedded high-resolution images, scanned pages, and vector graphics frequently cause file size bloat.

3.1 Where PDF Size Comes From

A PDF file's size is primarily determined by:

Embedded images and scans: A single 600DPI scanned page can reach 2-5MB; a 50-page document easily exceeds 100MB
Embedded fonts: A complete CJK font set can occupy 10-20MB
Vector graphics and redundant objects: CAD-exported vectors, replaced-but-not-deleted old images, hidden layers

3.2 Five Core PDF Compression Techniques

graph LR
    A[Input PDF] --> B[Image Resampling<br/>600DPI→150DPI]
    B --> C[Format Conversion<br/>FlateDecode→JPEG]
    C --> D[Font Subsetting<br/>keep used glyphs only]
    D --> E[Remove Redundant Objects<br/>clean unreferenced resources]
    E --> F[Linearization<br/>enable fast web view]
    F --> G[Output PDF<br/>80-90% size reduction]

    style A fill:#FFC107
    style G fill:#4CAF50,color:#fff
    style B fill:#E3F2FD
    style C fill:#E3F2FD
    style D fill:#E3F2FD
    style E fill:#E3F2FD
    style F fill:#E3F2FD

# PDF compression pseudocode
def compress_pdf(input_pdf, target_dpi=150, jpeg_quality=72):
    # 1. Image resampling: 600DPI → 150DPI (~90% data reduction)
    for image in input_pdf.images:
        image.resample(target_dpi)

    # 2. Image format conversion: FlateDecode (PNG-like) → DCTDecode (JPEG)
    for image in input_pdf.images:
        image.convert_to_jpeg(quality=jpeg_quality)

    # 3. Font subsetting: keep only characters actually used in the document
    for font in input_pdf.fonts:
        font.subset(input_pdf.used_characters)

    # 4. Remove redundant objects: clean unreferenced resources, hidden layers, old versions
    input_pdf.remove_redundant_objects()

    # 5. Linearization (Fast Web View): restructure for streaming load
    input_pdf.linearize()

    return input_pdf.save()

Real-world example: An 80MB scanned contract PDF (60 pages, 600DPI color scan), with a target of under 10MB for email:

Image resampling: 600DPI → 150DPI
Format conversion: FlateDecode → JPEG, quality 72%
Font subsetting: enabled (scanned document includes OCR layer)
Redundancy removal: clean duplicate objects and metadata from scanning
Grayscale conversion: color pages converted to grayscale (contract text doesn't need color)

Final result: 8.2MB, ~90% compression ratio, with text remaining clearly readable.

graph TD
    subgraph Before
        P1[80MB Scanned Contract PDF]
        P1 --> P1a[60 pages, 600DPI color scan]
        P1 --> P1b[Full embedded CJK fonts]
        P1 --> P1c[Redundant scan objects]
    end

    subgraph After
        Q1[8.2MB Optimized PDF]
        Q1 --> Q1a[60 pages, 150DPI JPEG]
        Q1 --> Q1b[Subset fonts]
        Q1 --> Q1c[Redundant objects cleaned]
    end

    P1 -->|90% compression| Q1

    style P1 fill:#FFC107
    style Q1 fill:#4CAF50,color:#fff

4. Image Compression

Images are the #1 culprit for file size bloat. A single smartphone photo can easily be 5-10MB, and a Word document with a dozen embedded images can quickly exceed 50MB.

4.1 Mainstream Image Format Comparison

Format	Compression Type	Transparency	Best For	Characteristics
JPEG	Lossy	No	Photos, gradients	High compression ratio, small size
PNG	Lossless	Yes	Icons, screenshots, transparent images	Full detail preserved, larger size
WebP	Lossy/Lossless	Yes	Web images	25-35% smaller than JPEG
TIFF	Lossless/Uncompressed	Yes	Printing, pro photography	Largest size, highest quality
BMP	Uncompressed	No	Rarely used	Huge size, not recommended

A simple rule of thumb: photos → JPEG, icons/screenshots → PNG, web images → WebP. Here's a more detailed decision flow:

graph TD
    A[Choose Image Format] --> B{Need transparency?}
    B -->|Yes| C{Need lossless?}
    B -->|No| D{What's the use case?}

    C -->|Yes| E[PNG<br/>icons/screenshots/UI]
    C -->|No| F[WebP<br/>web transparent images]

    D -->|Photos/gradients| G{For web use?}
    D -->|Line art/text| H[PNG<br/>avoid JPEG artifacts]

    G -->|Yes| I[WebP<br/>smallest size]
    G -->|No| J[JPEG<br/>best compatibility]

    style E fill:#4CAF50,color:#fff
    style F fill:#2196F3,color:#fff
    style I fill:#2196F3,color:#fff
    style J fill:#FF9800,color:#fff
    style H fill:#4CAF50,color:#fff

4.2 Key Parameters: Resolution and Quality Factor

# Image compression key parameters
from PIL import Image

img = Image.open("photo.jpg")  # Original 4000x3000, ~8MB

# Resolution adjustment: halve → ~75% size reduction
img_resized = img.resize((1920, 1080))

# Quality factor: 95→75, barely visible difference, ~50% size reduction
img_resized.save("photo_compressed.jpg", quality=75)
# Result: ~500KB, overall reduction ~94%

graph LR
    A[Original Photo<br/>4000×3000<br/>~8MB] --> B[Resolution Adjusted<br/>1920×1080<br/>~2MB]
    B --> C[Quality Factor Adjusted<br/>Q95→Q75<br/>~500KB]

    A --> D[Total reduction: 94%]

    style A fill:#FFC107
    style B fill:#FF9800,color:#fff
    style C fill:#4CAF50,color:#fff
    style D fill:#2196F3,color:#fff

Resolution: Scaling 4000×3000 down to 1920×1080 reduces size by 75%+
Quality factor: Dropping JPEG quality from 95 to 75 is virtually indistinguishable to the human eye, yet cuts size by 50%
Sweet spot: JPEG quality around 75 is sufficient for most use cases

5. Video Compression

Video files are the largest of all file types. A 1-minute 4K video can exceed 400MB, and even a 1080p screen recording can be over 100MB.

5.1 Codec Comparison

Codec	Compression Efficiency	Compatibility	Encoding Speed	Best For
H.264 (AVC)	Baseline	Excellent (nearly all devices)	Fast	General use, email, mobile
H.265 (HEVC)	40-50% smaller than H.264	Good (some older devices unsupported)	Medium	Storage archival, HD video
VP9	Close to H.265	Good (primarily web)	Medium	Web video, YouTube
AV1	20-30% smaller than H.265	Growing	Slow	Future standard, streaming

graph LR
    subgraph Increasing Compression Efficiency
        H264[H.264<br/>Baseline 100%] --> H265[H.265<br/>saves 40-50%]
        H265 --> VP9[VP9<br/>~H.265 level]
        VP9 --> AV1[AV1<br/>saves 20-30% more]
    end

    subgraph Decreasing Compatibility
        C1[H.264: nearly all devices] --> C2[H.265: most devices]
        C2 --> C3[VP9: mainly web]
        C3 --> C4[AV1: emerging]
    end

    style H264 fill:#4CAF50,color:#fff
    style AV1 fill:#FF9800,color:#fff

5.2 Bitrate Control Strategies

# FFmpeg video compression examples

# Email: H.264 + 720p + CRF 28 (~5-10MB for 1 min)
ffmpeg -i input.mp4 -c:v libx264 -crf 28 -preset medium \
  -vf scale=1280:720 -r 30 -c:a aac -b:a 128k output_email.mp4

# Web publishing: H.264 + 1080p + CRF 26
ffmpeg -i input.mp4 -c:v libx264 -crf 26 -preset medium \
  -vf scale=1920:1080 -r 30 -c:a aac -b:a 192k output_web.mp4

# Archival: H.265 + original resolution + CRF 20
ffmpeg -i input.mp4 -c:v libx265 -crf 20 -preset medium \
  -c:a aac -b:a 192k output_archive.mp4

Three bitrate control modes compared:

CRF (Constant Rate Factor): Quality stays constant, bitrate varies with scene complexity — most recommended
CBR (Constant Bitrate): Bitrate stays fixed, suitable for live streaming
VBR (Variable Bitrate): Dynamically adjusts within a specified range, ideal for precise target size control

graph TD
    A[Video Bitrate Control] --> B[CRF - Constant Rate Factor]
    A --> C[CBR - Constant Bitrate]
    A --> D[VBR - Variable Bitrate]

    B --> B1[Constant quality]
    B --> B2[Bitrate varies with complexity]
    B --> B3[Best for: general compression]
    B --> B4[Typical range: 20-28]

    C --> C1[Fixed bitrate]
    C --> C2[Wastes bandwidth on simple scenes]
    C --> C3[Best for: live streaming]

    D --> D1[Bitrate fluctuates within range]
    D --> D2[Precise target size control]
    D --> D3[Best for: storage-constrained scenarios]

    style B fill:#4CAF50,color:#fff
    style C fill:#FF9800,color:#fff
    style D fill:#2196F3,color:#fff

6. OFD Compression

OFD (Open Fixed-layout Document) is a document format defined by the Chinese national standard GB/T 33190-2016, widely used in government agencies and public institutions for electronic official documents. International teams working with Chinese partners may encounter this format in cross-border business.

6.1 OFD vs. PDF: Key Differences

Dimension	OFD	PDF
Standard	Chinese National Standard GB/T 33190-2016	ISO 32000 International Standard
File structure	XML + ZIP container, open structure	Binary/text mixed, relatively closed
Use cases	Government docs, electronic certificates, bidding	General documents, contracts, reports
Signature support	Native electronic seals, compliant with Chinese crypto standards	Digital signatures supported, Chinese crypto needs adaptation

6.2 OFD Compression Methods

An OFD file is essentially a ZIP archive containing XML files that describe pages, text, images, and other elements. Compression methods are similar to PDF:

Image resampling: Reduce high-resolution images to appropriate resolution (300dpi → 150dpi)
Font subsetting: Keep only the character glyphs actually used in the document
Clean redundant objects: Remove unreferenced resources and duplicates
Image format optimization: Convert uncompressed bitmaps to JPEG

A 50MB OFD scanned government document can typically be compressed to 8-12MB with proper processing.

graph TD
    A[OFD File Structure] --> B[ZIP Container Layer]
    A --> C[XML Description Layer]
    A --> D[Resource Layer]

    B --> B1[Doc_0/ document directory]
    B --> B2[Pages/ page directory]
    B --> B3[Res/ resource directory]

    C --> C1[Document.xml - doc description]
    C --> C2[Page_N.xml - page description]
    C --> C3[Signs/ - signature info]

    D --> D1[Image resources]
    D --> D2[Font resources]
    D --> D3[Color spaces]

    E[Compression Optimization] --> E1[Image resampling]
    E --> E2[Font subsetting]
    E --> E3[Remove unreferenced resources]
    E --> E4[Bitmap → JPEG]

    style A fill:#2196F3,color:#fff
    style E fill:#4CAF50,color:#fff

7. Local vs. Online Compression: Security Considerations

7.1 Fundamental Differences

graph LR
    subgraph Local Compression
        L1[User Device] --> L2[Local Compression Engine]
        L2 --> L3[Compressed File]
        L4[Files never leave the device]
    end

    subgraph Online Compression
        O1[User Device] -->|Upload file| O2[Third-party Server]
        O2 --> O3[Server-side compression]
        O3 -->|Download result| O4[User Device]
        O5[Files traverse network<br/>retention risk exists]
    end

    style L2 fill:#4CAF50,color:#fff
    style O2 fill:#FFC107
    style L4 fill:#E8F5E9
    style O5 fill:#FFF3E0

Dimension	Local Compression	Online Compression
Data location	Stays on local device	Uploaded to third-party server
Data transfer risk	None	Potential data exposure in transit
Third-party risk	None	Leakage and retention risk
Audit trail	Enterprise can log processing	Operations outside enterprise logging
Network dependency	None	Required

7.2 Compliance Considerations

With regulations like GDPR (EU), CCPA (California), PIPL (China's Personal Information Protection Law), and HIPAA (US healthcare), organizations must consider:

Data transfer risk: Using overseas online tools to process sensitive files may trigger regulatory reporting obligations
Third-party processing risk: Even if providers promise deletion, transmission logs and backup mechanisms may leave traces
Audit trail gaps: Many regulations require data processing logs to be retained for 6+ months — employees using personal online tools creates compliance blind spots

Core principle: classify data, prioritize local processing, maintain audit trails.

8. How to Choose a Compression Tool

When evaluating compression tools, consider these dimensions:

Dimension	Key Question
Format support	Can it handle PDF, images, video, Office documents, and more?
Compression ratio	How effective is compression at equal quality? Are multiple compression levels available?
Data security	Does it require file upload? Local or cloud processing?
Batch processing	Can it compress multiple files simultaneously? Queue management?
Platform support	Windows, macOS, Linux? Apple Silicon support?
Resource usage	Does compression impact other applications? Is CPU/memory usage controllable?
Cost	What are the free tier limits? Is the paid pricing reasonable?

9. FAQ

Q1: Will compression reduce image quality?

It depends on the compression method. Lossless compression won't reduce quality, but lossy compression trades some detail for smaller size. Choosing the right compression level balances size and quality. Generally, compressing PDF to 150dpi is perfectly fine for screen reading — the difference is virtually imperceptible.

Q2: What's the difference between 7-Zip and specialized content compression tools?

7-Zip is an archive compression tool that packages files into .7z/.zip archives — when extracted, the files are restored to their original size. Specialized content compression tools directly reduce the size of individual files (PDF, images, video) — the compressed file can be opened immediately without extraction. They serve different purposes: the former is for bundling, the latter for reducing individual file size.

Q3: What if my PDF got larger after compression?

This typically happens with PDFs that are already highly compressed. If internal images are already low-resolution JPEG, re-encoding may introduce overhead. Prioritize redundant object removal and font subsetting — lossless operations that avoid re-encoding already compressed images.

Q4: Can I compress PDFs without an internet connection?

Yes. Local compression software can run entirely offline — files never touch the network. Open-source options like Ghostscript and PyMuPDF can perform local PDF compression via command line or programmatically, suitable for handling sensitive or classified documents. Commercial software typically offers friendlier interfaces and multi-format support.

Q5: What's the optimal JPEG quality factor?

Generally, JPEG quality 75 is the sweet spot for balancing size and visual quality. Above 75, the human eye can barely tell the difference; below 75, noticeable blocky artifacts begin to appear. For extreme compression needs (like email attachments), 60 is acceptable; for high-quality preservation (like photography portfolios), 90+ is recommended.

Summary

File compression isn't rocket science, but choosing the right method can save you enormous time and storage. The core principles:

Know your goal: Email / web publishing / archival — different scenarios call for different parameters
Choose the right approach: Lossy vs. lossless, archive vs. content compression
Prioritize data security: Choose local compression for sensitive files to avoid compliance risks

Quick reference table:

File Type	Recommended Parameters	Expected Compression
Scanned PDF	600DPI → 150DPI + JPEG Q72	80-90%
Photos	Halve resolution + JPEG Q75	75-94%
Video for email	H.264 + 720p + CRF 28	90-95%
Video for web	H.264 + 1080p + CRF 26	80-90%
Video for archival	H.265 + original resolution + CRF 20	50-70%
OFD documents	Image resampling + font subsetting	75-85%

This is an original technical article. Feel free to share and repost with attribution.

DEV Community: Uglypear Data

The Complete Guide to File Compression: PDF, Image, Video & Document Compression Explained

The Complete Guide to File Compression: PDF, Image, Video & Document Compression Explained

1. Fundamentals of File Compression

1.1 Lossless Compression

1.2 Lossy Compression

2. Archive Compression vs. Content Compression: A Critical Distinction

3. PDF Compression

3.1 Where PDF Size Comes From

3.2 Five Core PDF Compression Techniques

4. Image Compression

4.1 Mainstream Image Format Comparison

4.2 Key Parameters: Resolution and Quality Factor

5. Video Compression

5.1 Codec Comparison

5.2 Bitrate Control Strategies

6. OFD Compression

6.1 OFD vs. PDF: Key Differences

6.2 OFD Compression Methods

7. Local vs. Online Compression: Security Considerations

7.1 Fundamental Differences

7.2 Compliance Considerations

8. How to Choose a Compression Tool

9. FAQ

Q1: Will compression reduce image quality?

Q2: What's the difference between 7-Zip and specialized content compression tools?

Q3: What if my PDF got larger after compression?

Q4: Can I compress PDFs without an internet connection?

Q5: What's the optimal JPEG quality factor?

Summary