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AI Upscaling Benchmark

4x Upscaling Performance Analysis

Important Context: Processing times vary significantly based on quality settings, hardware configuration, image content, and other factors. This benchmark provides baseline performance data under controlled conditions but actual results may differ based on your specific configuration and requirements.

Benchmark Configuration

  • Upscaling Factor: 4x (consistent across all tests)
  • Quality Settings: Identical standard configuration for all image categories
  • Sample Set: Random selection from each size category
  • Total Images Tested: 25,936 images
  • Hardware: Performance data from controlled test environment (Nvidia RTX A4000)
  • Processing Mode: Standard quality settings (not optimized for speed or maximum quality)

Processing Time by Image Size Category

Image CategoryResolution RangeAvg. Megapixels4x Upscaling TimeFiles Tested
Smallsmaller than 500×5000.13 MP3.0 seconds297
Medium500×500 to 1000×10000.47 MP4.9 seconds7,433
Large1000×1000 to 2000×20001.15 MP10.5 seconds17,595
Very Large2000×2000 to 3000×30005.89 MP41.2 seconds426
Extra Largelarger than 3000×300026.73 MP167.2 seconds185

Performance Insights

📊 4x Upscaling Performance Scaling

With consistent 4x upscaling factor and identical quality settings:

  • Extra Large images take 56× longer to process than Small images
  • Megapixel difference is 205× larger (0.13 MP → 26.73 MP)
  • Non-linear scaling: Computational complexity grows exponentially beyond the linear megapixel increase
  • Output size impact: 4x upscaling means Small images (0.13 MP) output at ~2.1 MP, while Extra Large (26.73 MP) output at ~427 MP

⚠️ Performance Variables: Actual processing times will vary based on:

  • Image content complexity (faces, textures, patterns)
  • Quality vs. speed configuration (fast mode vs. high-quality mode)
  • Enhancement intensity settings (Super Resolution, Facial Reconstruction)
  • Hardware specifications (GPU model, memory, CPU)

⚡ 4x Upscaling Throughput by Category

Small Images (Optimal for High-Volume 4x Upscaling)

  • Processing time: ~3 seconds per 4x upscaling
  • High throughput: ~1,200 images/hour potential
  • Output quality: 500×500 → 2000×2000 typical
  • Optimal for: Batch thumbnail upscaling, web image enhancement

Medium Images (Balanced 4x Performance)

  • Processing time: ~5 seconds per 4x upscaling
  • Moderate throughput: ~720 images/hour
  • Output quality: 1000×1000 → 4000×4000 typical
  • Optimal for: Standard photo enhancement, social media upscaling

Large Images (Standard 4x Production)

  • Processing time: ~10.5 seconds per 4x upscaling
  • Standard throughput: ~340 images/hour
  • Output quality: 2000×2000 → 8000×8000 typical
  • Optimal for: Professional photography, large format printing

Very Large Images (Intensive 4x Processing)

  • Processing time: ~41 seconds per 4x upscaling
  • Lower throughput: ~87 images/hour
  • Output quality: 3000×3000 → 12000×12000 typical
  • Optimal for: High-end photography, large format printing

Benchmark Methodology & Performance Factors

🎯 Understanding This Benchmark

  • Controlled conditions: Standardized 4x upscaling with identical quality settings
  • Random sampling: Eliminates content bias in processing time measurements
  • Baseline performance: Represents standard configuration, not speed-optimized or maximum quality
  • Large sample sizes: Statistically reliable averages (especially 7,433 medium and 17,595 large images)

⚙️ Factors That Affect Real-World Performance and quality

Quality vs. Speed Configuration

  • Fast Mode: Significantly faster processing with reduced quality
  • High Quality Mode: Longer processing times with enhanced output quality
  • Standard Mode: Balanced approach (used in this benchmark)

Image Content Complexity

  • Face detection & reconstruction: More faces = longer processing time
  • Texture complexity: Detailed textures require more computational resources
  • Image compression: Higher compression may require artifacts removal

Hardware Configuration Impact

  • GPU Performance: Higher-end GPUs dramatically reduce processing times
  • Memory Availability: Insufficient VRAM can cause significant slowdowns
  • CPU Performance: Affects overall system responsiveness during processing
  • Storage Speed: SSD vs. HDD can impact file I/O performance

Parameter Optimization Opportunities

  • Super Resolution Intensity: Lower values increase speed, higher values improve naturalness
  • Facial Reconstruction Settings: Can be tuned for speed vs. quality trade-offs
  • Processing & Blending Modes: Different modes offer various speed/quality balances

📊 Hardware Performance Reference

Nvidia RTX A4000 (16GB Ada Architecture):

  • Daily throughput: ~6,000 to ~10,000 images
  • Configuration: Medium upscaling factor (2.4x), medium input sizes
  • Usage: Production environment with balanced quality settings

Key Takeaways for Production Planning

🚀 No Universal Processing Time

Each image's processing time varies significantly based on multiple interdependent factors. Use this benchmark as a baseline, but expect variations in real-world scenarios.

⚖️ Quality vs. Speed Trade-off

  • This benchmark uses standard settings - you can achieve faster processing with speed-optimized configurations
  • Higher quality settings will increase processing times beyond these baseline measurements
  • Parameter tuning is essential for optimizing your specific workflow requirements

💻 Hardware Impact

  • GPU performance directly affects throughput - better hardware can significantly reduce these processing times
  • Memory constraints can cause dramatic performance degradation
  • Consider hardware upgrade ROI when planning high-volume processing workflows

📈 Scalability Considerations

  • Small-Medium images: Suitable for real-time or interactive workflows with appropriate hardware
  • Large images: Plan for moderate delays in production pipelines
  • Very Large+ images: Best suited for batch processing during off-peak hours
  • Processing complexity grows non-linearly - plan resources accordingly for large image batches

Technical Benchmark Summary

Controlled Test Dataset: 25,936 randomly sampled images

  • Hardware: Nvidia RTX A4000
  • Configuration: 4x upscaling factor, standard quality settings across all tests
  • Average file size range: 0.20 MB (small) to 3.01 MB (very large)
  • Input megapixel range: 0.13 MP to 26.73 MP (205× difference)
  • Standard 4x upscaling time range: 3.0s to 167.2s (56× difference)
  • Output megapixel range: ~2.1 MP to ~427 MP after 4x enhancement

Benchmark Limitations: This data represents standard configuration performance under controlled conditions. Actual production results will vary based on your specific quality settings, hardware configuration, image content complexity, and optimization choices. Use these figures as baseline estimates for planning purposes, and conduct your own testing with representative images and target configurations for accurate performance projections.