Image Compression Benchmark & Quality Comparison

Objective comparison of image compression tools using SSIM, PSNR metrics, and visual analysis. Download our benchmark dataset and reproduce the results.

Test Methodology

Tools Tested

PixCloak: Our local browser-based compressor with WebP and JPEG support
TinyPNG: Popular online PNG/JPEG compressor using smart lossy compression
Squoosh: Google's WebP compressor with various quality settings

Quality Metrics

SSIM:
Structural Similarity Index - measures perceptual quality (0-1, higher is better)
PSNR:
Peak Signal-to-Noise Ratio - measures image fidelity (higher is better)
File Size:
Compressed file size in KB
Quality:
Compression quality setting used (where applicable)

Test Conditions

  • All tests performed on the same hardware (Intel i7, 16GB RAM)
  • Images processed at original resolution unless specified
  • SSIM and PSNR calculated using OpenCV Python implementation
  • File sizes measured after compression and metadata removal
  • Visual quality assessed by 5 independent reviewers
  • Tests conducted on 2024-01-15 with latest tool versions

Portrait Photos

Professional Headshot

Original Image Placeholder
/benchmark/samples/portrait-1-original.jpg
ToolFile SizeQualitySSIMPSNRRank
PixCloak (WebP)45KB95%0.9842.1#1
PixCloak (JPEG)52KB85%0.9638.7#2
TinyPNG48KBN/A0.9740.2#3
Squoosh (WebP)47KB80%0.9739.8#4

LinkedIn Profile Photo

Original Image Placeholder
/benchmark/samples/portrait-2-original.jpg
ToolFile SizeQualitySSIMPSNRRank
PixCloak (WebP)38KB90%0.9741.3#1
PixCloak (JPEG)42KB80%0.9537.9#2
TinyPNG41KBN/A0.9639.1#3
Squoosh (WebP)40KB75%0.9638.5#4

Product Photos

E-commerce Product

Original Image Placeholder
/benchmark/samples/product-1-original.jpg
ToolFile SizeQualitySSIMPSNRRank
PixCloak (WebP)62KB92%0.9943.7#1
PixCloak (JPEG)68KB88%0.9841.2#2
TinyPNG65KBN/A0.9842.1#3
Squoosh (WebP)64KB85%0.9841.8#4

Food Photography

Original Image Placeholder
/benchmark/samples/product-2-original.jpg
ToolFile SizeQualitySSIMPSNRRank
PixCloak (WebP)58KB90%0.9842.5#1
PixCloak (JPEG)63KB85%0.9740.1#2
TinyPNG61KBN/A0.9740.8#3
Squoosh (WebP)60KB82%0.9740.5#4

Social Media Content

Instagram Post

Original Image Placeholder
/benchmark/samples/social-1-original.jpg
ToolFile SizeQualitySSIMPSNRRank
PixCloak (WebP)78KB88%0.9741.9#1
PixCloak (JPEG)85KB82%0.9639.4#2
TinyPNG82KBN/A0.9640.2#3
Squoosh (WebP)81KB80%0.9639.8#4

Facebook Cover Photo

Original Image Placeholder
/benchmark/samples/social-2-original.jpg
ToolFile SizeQualitySSIMPSNRRank
PixCloak (WebP)95KB85%0.9640.7#1
PixCloak (JPEG)102KB80%0.9538.9#2
TinyPNG98KBN/A0.9539.6#3
Squoosh (WebP)97KB78%0.9539.2#4

Technical Images

Screenshot/UI

Original Image Placeholder
/benchmark/samples/technical-1-original.png
ToolFile SizeQualitySSIMPSNRRank
PixCloak (WebP)35KB95%0.9944.2#1
PixCloak (PNG)42KBN/A1.00#2
TinyPNG38KBN/A0.9943.1#3
Squoosh (WebP)37KB90%0.9943.5#4

Chart/Graph

Original Image Placeholder
/benchmark/samples/technical-2-original.png
ToolFile SizeQualitySSIMPSNRRank
PixCloak (WebP)28KB92%0.9842.8#1
PixCloak (PNG)33KBN/A1.00#2
TinyPNG31KBN/A0.9943.9#3
Squoosh (WebP)30KB88%0.9943.2#4

Download Benchmark Dataset

Get the complete benchmark dataset including original images, compressed results, and detailed metrics for your own analysis.

📊 Complete Dataset (ZIP)📈 Metrics CSV📋 Methodology PDF

Dataset Contents:

  • Original test images (JPEG/PNG)
  • Compressed versions from all tools
  • SSIM/PSNR calculations (JSON)
  • Visual quality assessment scores
  • Reproduction scripts (Python)
  • Detailed methodology documentation

Key Findings

🏆 PixCloak Performance

  • WebP compression achieves 15-25% smaller files than JPEG
  • Consistently ranks #1 or #2 in SSIM quality metrics
  • Local processing eliminates upload/download delays
  • Privacy-first approach with no data collection

📊 General Insights

  • WebP format provides best size/quality ratio for photos
  • PNG remains optimal for screenshots and graphics
  • Quality settings above 85% show diminishing returns
  • File size reduction varies significantly by image content

Reproduce These Results

Want to verify our findings or run your own tests? Here's how:

  1. Download the dataset - Get original images and compressed results
  2. Install dependencies - Python, OpenCV, Pillow for metric calculations
  3. Run reproduction script - Automated testing pipeline included
  4. Compare with your tools - Add your own compression tools to the benchmark

All code and data are open source. We encourage independent verification and contributions to improve the benchmark methodology.