Complete Restoration of Deep Zoom Images: Converting Tile Images to BigTIFF

Introduction

Deep Zoom technology is used to smoothly zoom and display high-resolution images on websites. There are cases where you need to restore the original high-resolution image from tiled image data generated by tools such as Microsoft Deep Zoom Composer.

This article explains the technology for restoring original high-resolution TIFF images from image data published in Deep Zoom format.

How Deep Zoom Images Work

Tile Structure

Deep Zoom images divide a single large image into multiple small tile images and store them in a pyramid structure:

• Level 0: Lowest resolution (usually 1 tile)
• Level N: Highest resolution (equivalent to the original image resolution)
• Resolution doubles at each level

File Structure

Implementation Challenges and Solutions

Challenge 1: XML Metadata Namespace Differences

Deep Zoom has multiple versions with different XML namespaces:

• http://schemas.microsoft.com/deepzoom/2008
• http://schemas.microsoft.com/deepzoom/2009

Solution: Implement a flexible XML parser supporting multiple namespaces

Challenge 2: Auto-Detection of Maximum Level

The maximum level stated in the XML may differ from the levels actually available on the server.

Solution: Send HEAD requests to verify existence

Challenge 3: Efficient Download of Large Numbers of Tiles

High-resolution images require downloading tens of thousands of tiles (e.g., 29,146 tiles).

Solution: Parallel download using ThreadPoolExecutor

Challenge 4: Handling Tile Overlap

Deep Zoom tiles have overlap (overlapping areas) for seamless display.

Solution: Coordinate calculation considering overlap

Challenge 5: Saving Large Images

Restored images can be several GB in size, potentially exceeding the standard TIFF 4GB limit.

Solution: Save in BigTIFF format

Implementation Results

Processing Performance

Parallel Processing Effectiveness

• Parallelism: 10 threads
• Average download speed: Approx. 40 tiles/sec
• Efficient use of network bandwidth

Technology Stack

Code Structure

Optimization Points

1. Session Reuse

2. Error Handling

3. Memory Efficiency

• Process tile by tile to minimize memory usage
• Create the large canvas only once

Use Cases

Digital Archives

• High-resolution image preservation of historical documents and artworks
• Complete restoration of map data
• Digital preservation of cultural assets

Data Migration

• Image data conversion during platform migration
• Complete image acquisition for backup purposes
• Image usage in offline environments

Summary

The restoration of Deep Zoom images involved the following technical challenges, which were successfully addressed:

1. Handling XML namespace differences
2. Auto-detection of actual maximum levels
3. Parallel download of large numbers of tiles
4. Image restoration considering overlap
5. Large image storage in BigTIFF format

Using this method, ultra-high-resolution images of 60,000 x 30,000 pixels could be completely restored in approximately 12 minutes.

References

• Microsoft Deep Zoom Specification
• PIL/Pillow Documentation
• tifffile Library Documentation
• Python concurrent.futures

Note: The techniques described in this article should only be used on image data for which you have appropriate permissions. Please ensure compliance with copyright and licensing requirements.