Our tool employs multiple compression algorithms selected based on image type and target size:

• JPEG Compression: Uses optimized quantization tables based on psychovisual research, progressive encoding for better perceived loading, and optimized Huffman coding for efficient entropy encoding.
• WebP Compression: Leverages predictive coding (VP8 key frame encoding), partitioning of the image into segments, and advanced entropy coding. Supports both lossy and lossless compression.
• PNG Optimization: Implements palette reduction, optimal filter selection per scanline, and DEFLATE compression with optimized compression levels.
• Proprietary Enhancements: We've developed additional optimizations including content-aware quantization, adaptive smoothing algorithms, and perceptual quality-guided compression.

The size targeting uses an iterative optimization approach:

1. Initial Analysis: The algorithm analyzes image complexity, calculates theoretical minimum file size, and sets initial compression parameters.
2. Prediction Phase: Using machine learning models trained on thousands of images, it predicts what compression settings will achieve the target size.
3. Iterative Refinement: The image is compressed with predicted settings, then actual size is measured. If outside tolerance (±5%), parameters are adjusted and the process repeats.
4. Convergence: The algorithm typically converges in 3-7 iterations, balancing compression level, quantization, and format-specific optimizations.

This approach achieves target sizes with 95% accuracy while maximizing quality for that size constraint.

Understanding this distinction is crucial for choosing the right compression:

Our tool automatically selects the appropriate method based on image content and your quality settings.

We use multiple quality assessment methods:

• SSIM (Structural Similarity Index): Measures perceptual similarity between original and compressed images (0-1 scale). We aim for >0.95 for high quality, >0.90 for balanced.
• PSNR (Peak Signal-to-Noise Ratio): Traditional metric measuring error introduced (higher is better). Target >40dB for high quality.
• Butteraugli: Google's perceptual diff algorithm that closely matches human visual perception.
• Visual Artifact Detection: Algorithms that specifically detect and minimize blocking artifacts, ringing, color banding, and blurring.

The compression algorithm uses these metrics to guide parameter adjustments, prioritizing quality preservation in visually important areas.

Here are recommended size targets for common platforms:

• Website Hero Images: 150-200KB, width 1920px, WebP format preferred
• Blog Content Images: 50-100KB, width 1200px, quality 75-85%
• Instagram Feed: 100-150KB, 1080px width, JPG with optimized compression
• Facebook Posts: 100-200KB, 1200px width, avoid Facebook's recompression
• Email Newsletters: 30-70KB, 600px width, baseline JPEG for compatibility
• Product Thumbnails: 10-30KB, 400px width, aggressive compression acceptable
• PDF Documents: 50-150KB per image, 150-300 DPI, JPG medium quality

Our tool includes presets for these common use cases in the Pro version.

While the free version processes single images, here are approaches for batch compression:

1. Manual Batch: Process images one by one using the same settings for consistency
2. Pro Version: Upload multiple images (up to 50), apply uniform or individual settings, download as ZIP
3. Automated Workflow: Use our API (available in Pro) to integrate compression into your CMS, e-commerce platform, or development pipeline
4. Desktop Alternative: For very large batches, consider desktop software like ImageOptim, Squoosh.app, or using command-line tools like ImageMagick with our recommended settings

For occasional batch needs, the manual approach with consistent settings works well. For regular bulk compression, the Pro version saves significant time.