The colour depth – so the correct number of bits are used to represent the colour of each pixel.Īn image with the CORRECT colour depth metadata – 1 bit colour depth.The resolution – so the image displays at the correct size.
#Binary smallimage software#
Metadata is needed in a bitmap image file because the software that displays an image needs to know: Why does metadata need to be included in an image file?Ī typical example of the metadata stored in a digital image Also, if a vector image was scaled-up and then saved it would have the same file size as the original. The result is that the file size of a vector image would be considerably smaller than the equivalent bitmap image which would need to store the colour information for every pixel.Īlthough vector images can have graduated fills these are mathematically defined and cannot represent the level of detail needed for photographic images.Īs well as the smaller file sizes, vector images have the advantage that they can be scaled to any size without the loss of detail that would occur with a bitmap image. This information would be stored using binary codes for the instructions. For example, to save a vector image of a circle the software only needs to store: Vector images store information as mathematical instructions rather than as individual pixels. There are also hybrid use cases, such as storing large images (for example, raw images from a digital camera or phone) in the database and then passing those to an API or image processing library to create resized versions which will be served directly to end users.īefore deciding to store images in your database, I would make sure there is a clear benefit for the intended use case.Vector and bitmap images compared, showing the effect of magnification on each A downside of using references is that they can get out of sync with the source document. There is less overhead serving images directly from a web server versus going through an application server and database server for every request. If images or large binary assets are being served directly to end users, the Reference approach is usually most suitable because files can be pushed out to an API and/or CDN (Content Delivery Network) and cached/resized for better user experience. Aside from the GridFS documentation page that has already been linked in an earlier comment, Building MongoDB Applications with Binary Files using GridFS ( part 1 and part 2) may also be helpful reading. Storing binary files in a database can be convenient for distributing across multiple locations (via replication), for working around file system limitations (eg files per directory or file naming), for serving streaming or protected content, or for storing larger assets that aren’t going to be served directly to end users. Reference: As suggested by images can be saved to an API or filesystem, with only the image reference stored in the database. Inline: As suggested by smaller images (within the 16MB document size limit) can be stored directly in a MongoDB document using the BinData (binary data) BSON type. For more info on the implementation, see the GridFS spec on GitHub. The GridFS API is a client-side implementation – a MongoDB deployment doesn’t have any special configuration for the underlying collection data. This API is supported by official MongoDB drivers: it splits large files into smaller chunks (255KiB by default) which are stored as separate documents in an fs.chunks collection with a reference document including metadata in an fs.files collection (note: the default fs.* namespace can be changed). GridFS: As suggested by large images (or binary blobs) can be stored using the GridFS API.
Welcome to the MongoDB Community the earlier suggestions, there are three common approaches for working with images and other binary assets:
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