Cloudinary vs dicom | What are the differences? | StackShare

Cloudinary vs dicom

Overview

Cloudinary

Stacks634

Followers594

Votes179

dicom

Stacks23

Followers10

Votes0

GitHub Stars1.0K

Forks148

Cloudinary vs dicom: What are the differences?

Developers describe Cloudinary as "An end-to-end image & video management solution for your web and mobile applications". Cloudinary is a cloud-based service that streamlines websites and mobile applications' entire image and video management needs - uploads, storage, administration, manipulations, and delivery. On the other hand, dicom is detailed as "A High Performance DICOM Medical Image Parser in Go". It is a golang DICOM image parsing library and command line tool. Its features include parsing and extracting multi-frame DICOM imagery (both encapsulated and native pixel data), exposing a Parser golang interface to make mock-based testing easier for clients etc.

Cloudinary and dicom belong to "Image Processing and Management" category of the tech stack.

dicom is an open source tool with 344 GitHub stars and 17 GitHub forks. Here's a link to dicom's open source repository on GitHub.

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Detailed Comparison

Cloudinary	dicom
Cloudinary is a cloud-based service that streamlines websites and mobile applications' entire image and video management needs - uploads, storage, administration, manipulations, and delivery.	It is a golang DICOM image parsing library and command line tool. Its features include parsing and extracting multi-frame DICOM imagery (both encapsulated and native pixel data), exposing a Parser golang interface to make mock-based testing easier for clients etc.
Upload images to a cloud-based storage; Upload video to cloud-based storage; Automatic backup and revision tracking; Many image manipulation capabilities & effects; Smart resizing & face detection based thumbnails; Handling PDFs, sprites, watermarks, social profile pictures; Fast CDN delivery with advanced caching; Automatic image optimization; Powerful dashboard, media library and reports; RESTful APIs, intuitive URL based transformations; Image metadata and semantic data extraction; Integration SDKs for web & mobile frameworks; Custom domain (CNAME) support; Authenticated URLs; On-the-fly Video transcoding ; On-the-fly Video overlays;	high-performance
Statistics
GitHub Stars -	GitHub Stars 1.0K
GitHub Forks -	GitHub Forks 148
Stacks 634	Stacks 23
Followers 594	Followers 10
Votes 179	Votes 0
Pros & Cons
Pros 37 Easy setup 31 Fast image delivery 26 Vast array of image manipulation capabilities 21 Free tier 11 Heroku add-on Cons 5 Paid plan is expensive	No community feedback yet
Integrations
Microsoft Azure AppHarbor Heroku Node.js Django WordPress Google App Engine Ruby Python Java	Golang Docker

What are some alternatives to Cloudinary, dicom?

imgix

imgix is the leading platform for end-to-end visual media processing. With robust APIs, SDKs, and integrations, imgix empowers developers to optimize, transform, manage, and deliver images and videos at scale through simple URL parameters.

OpenCV

OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.

ImageKit

ImageKit offers a real-time URL-based API for image & video optimization, streaming, and 50+ transformations to deliver perfect visual experiences on websites and apps. It also comes integrated with a Digital Asset Management solution.

Cloudimage

Effortless image resizing, optimization and CDN delivery. Make your site fully responsive and really fast.

scikit-image

scikit-image is a collection of algorithms for image processing.

Kraken.io

It supports JPEG, PNG and GIF files. You can optimize your images in two ways - by providing an URL of the image you want to optimize or by uploading an image file directly to its API.

ImageEngine

ImageEngine is an intelligent Image CDN that dynamically optimizes image content tailored to the end users device. Using device intelligence at the CDN edge, developers can greatly simplify their image management process while accelerating their site.

FFMPEG

The universal multimedia toolkit.

GStreamer

It is a library for constructing graphs of media-handling components. The applications it supports range from simple Ogg/Vorbis playback, audio/video streaming to complex audio (mixing) and video (non-linear editing) processing.

GraphicsMagick

GraphicsMagick is the swiss army knife of image processing. Comprised of 267K physical lines (according to David A. Wheeler's SLOCCount) of source code in the base package (or 1,225K including 3rd party libraries) it provides a robust and efficient collection of tools and libraries which support reading, writing, and manipulating an image in over 88 major formats including important formats like DPX, GIF, JPEG, JPEG-2000, PNG, PDF, PNM, and TIFF.