MKV is too big for spreadsheets. Here are the numbers.
Learn why MKV to JSON doesn't work and discover the right alternatives.
← Back to Converter💭 Let's Be Real...
Converting MKV to JSON is like trying to pour a video game into a notebook. Media files are rich, continuous streams of information. Spreadsheets need structured, tabular data that fits in cells. Without specialized metadata extraction or analysis tools, there's no meaningful conversion.
🔍 Understanding the Formats
What is MKV?
MKV (Matroska Video) - MKV (Matroska Video) is an open-source, royalty-free multimedia container supporting unlimited video, audio, and subtitle tracks within a single file. The format is based on EBML (Extensible Binary Meta Language) and supports any video codec (H.264, H.265, VP9, AV1) and audio codec (AAC, AC3, DTS, FLAC, Opus). MKV enables chapters, metadata, attachments (fonts, cover art), and menu systems. The format has no file size limitations and supports error recovery through segmentation. MKV is codec-agnostic and provides extensive flexibility for multi-language content distribution. Playback compatibility includes VLC, MPV, and most desktop media players, with limited support on mobile and streaming devices. MKV is widely used for high-quality video archival and anime/film distribution.
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
MKV is a video format containing video with audio. 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
MKV 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 MKV 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.