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Announcing imagezero_transport, a fast, lossless image transport plugin

ImageZero is a lossless, very fast compression algorithm for 24-bit color photographic images. Its compression ratio is comparable to PNG, but it compresses over twenty times faster and decompresses almost twice as fast.

I’ve made a new ROS stack that makes it easy to use ImageZero as a transport mechanism for any ROS topics that are already using the image_transport mechanism. It’s available on GitHub at, and it has been successfully built in the ROS Shadow repository, so it should be available in the next releases of ROS Indigo, Jade, and Kinetic. The imagezero_transport stack provides three packages:

  • imagezero: The original ImageZero algorithm built as a shared library using catkin
  • imagezero_ros: A shared library that provides convenience methods for converting between sensor_msgs/Image and sensor_msgs/CompressedImage messages using ImageZero
  • imagezero_image_transport: A plugin for the ROS image_transport package which adds an /imagezero sub-topic to ImageTransport topics that can be used to transparently use ImageZero to compress your images

For a little bit of backstory and a use case, I’m working on a system that is doing image processing on 10 Hz video feeds coming from stereo cameras connected to one computer than is publishing the video across a network as ROS Image topics. The uncompressed image data was consuming over 400 Mbps of bandwidth by itself, which was enough to choke other services on the 1 Gbps network. We looked at using the built-in JPG and PNG image transports, but JPG was unsuitable for our image processing algorithms because it’s lossy and PNG was unusable because it is so CPU-intensive that the host computer’s processor could not compress the image stream in real time.

After searching around for a while for image compression algorithms, I found ImageZero, which suits our needs perfectly. It’s lossless, designed explicitly for 24-bit color natural photography, and fast enough that our processors can easily do it on a real-time video feed. The bandwidth from our cameras is now down to about 150 Mbps.

Hopefully other people find this as useful as I have!

P. J. Reed, Senior Research Analyst
(210) 522-6948
Intelligent Vehicle Systems
Southwest Research Institute


Cool :slight_smile:.

It might be worth adding a note on the image_transport wiki page which points out there are other options for compression like this one:

Feel free to add a pointer to your documentation.

It might also make sense to add a tutorial on how to use your transport:

It would probably fit in nicely with the tutorial about adding a new transport type.