You can't put a movie in a spreadsheet. Excel would cry.
Learn why SVG to JSON doesn't work and discover the right alternatives.
← Back to Converter💭 Let's Be Real...
Converting SVG to JSON is like trying to fit an ocean into a coffee cup. Your SVG contains vector graphics. JSON is designed for rows and columns of data. These things are not compatible, no matter how much you believe in them. It's like asking a filing cabinet to play music - fundamentally the wrong tool for the job.
🔍 Understanding the Formats
What is SVG?
SVG (Scalable Vector Graphics) - SVG (Scalable Vector Graphics) is an XML-based vector image format standardized by W3C. The format defines images using mathematical descriptions of shapes, paths, text, and colors rather than pixel data. SVG supports Bézier curves, geometric primitives, gradients, patterns, filters, and clipping paths. Images scale infinitely without quality degradation, maintaining sharpness at any resolution. File size depends on vector complexity rather than image dimensions. SVG enables embedded JavaScript for interactivity, CSS for styling, and SMIL for animations. The format is resolution-independent and suitable for logos, icons, diagrams, and responsive web graphics. SVG files are human-readable text documents that can be edited in text editors or specialized vector graphics software.
What is JSON?
JSON (JavaScript Object Notation) - JSON (JavaScript Object Notation) stores hierarchical data structures using key-value pairs with syntax derived from JavaScript object notation. The format supports objects ({}), arrays ([]), strings, numbers, booleans, and null values. JSON enables nested data structures, making it suitable for complex data like API responses, configuration files, and NoSQL database documents. MongoDB uses BSON (Binary JSON) as its native format. JSON is language-independent despite JavaScript origins and serves as the standard data interchange format for REST APIs. The format is human-readable and lighter-weight than XML alternatives.
❌ Why This Doesn't Work
SVG is a image format containing vector graphics. JSON is a data format for structured data - numbers, text, formulas. Media doesn't fit into cells. It just doesn't. While you could extract metadata (file properties) or analyze media (like audio frequencies or image histograms), that requires specialized analysis software, not file conversion.
🔬 The Technical Reality
SVG media stores massive amounts of continuous binary data. Audio example: a 3-minute MP3 at 44.1kHz = 7,938,000 samples. Image example: a 1920×1080 PNG = 2,073,600 RGB pixels = 6,220,800 individual color values. Video example: a 10-second 1920×1080 MOV at 30fps = 300 frames = 622,080,000 pixels total. JSON spreadsheets have hard limits (XLSX: 1,048,576 rows × 16,384 columns = 17,179,869,184 cells maximum). A single second of 44.1kHz stereo audio would require 88,200 spreadsheet rows. A 1-second video at 1920×1080 30fps would need 1,866,240,000 cells for RGB data. These numbers exceed practical usability without specialized metadata extraction or AI analysis tools.
🤔 When Would Someone Want This?
People search for SVG to JSON conversion when they want to extract metadata, analyze media properties, or catalog media files. Photographers might want EXIF data from images. Audio engineers might want frequency analysis. Video editors might want frame-by-frame data. However, this requires specialized analysis tools that extract specific information from media - not simple file converters that change formats.
⚠️ What Would Happen If We Tried?
If we forced this, what would even go in the spreadsheet? Pixel values? Audio samples? You'd end up with millions of numbers that mean nothing to a human. It would be like trying to read The Matrix. Possible? Technically. Useful? Absolutely not. A single second of audio at 44.1kHz would create 44,100 rows. A 1920x1080 image would need 2,073,600 cells for RGB values. Your spreadsheet would explode.
🛠️ Tools for This Task
**Best for metadata:** ExifTool (images/video), MediaInfo (all media types). **Best for audio analysis:** Audacity, Sonic Visualiser. **Best for image analysis:** ImageJ, GIMP histogram. **Best for video data:** FFmpeg, MediaInfo. **Best for programmatic extraction:** Python librosa (audio), OpenCV (images/video). Choose based on data type: metadata for file properties, analysis tools for content properties, programming libraries for bulk processing.