One thing I’ve learned from my years in Machine Vision is that performance and bandwidth are never enough to satisfy leading edge applications; hence the constant quest to increase the transmission speed of camera interfaces. Teledyne DALSA has always been at the forefront of this chase and the GigE Vision standard is no different: GigE Vision 2.0′s primary mandate was to find ways to increase the bandwidth between camera and PC.
For obvious reasons, people looking to increase bandwidth take a straightforward approach to the problem: simply increase clock speed on the cable! For GigE Vision, this leads to the usage of 10 gigabit Ethernet. This is certainly a valid approach – although an expensive one, may I say. But one can look at this problem from another perspective: if the goal is to transfer more images in a shorter period of time, why not consider image compression?
You are going to say image compression leads to loss of information. And you are right. Compression means throwing away “less important” information, typically the high frequency content. But a study from Ebrahimi, Chamik and Winkler (see reference below) teaches us that the most advanced image quality authority, the human eye, does not see any visible degradation when using JPEG with a compression ratio less than 5. And if your eyes cannot see compression artifacts when they can definitively see sensor-induced noise, wouldn’t you consider image compression a valid option to reduce bandwidth? Additionally, most image compression algorithms, to a greater or lesser degree, filter out high frequency content, and noise typically fits in this category.
GigE Vision 2.0 (a free download from the AIA web site) is the first Machine Vision camera standard to natively enable usage of image compression. It introduces support for JPEG, JPEG 2000 and H.264. I admit data compression is not suitable for all applications, but this is an interesting low cost way to look at increasing the frame rate between the camera and the PC, with the added benefit that data archiving frequently relies on compression. So the next time you need to increase transmission bandwidth, you might open your wallet or you might take a look at alternative strategies, such as image compression.
Reference: JPEG vs JPEG2000: An Objective Comparison of Image Encoding Quality, Farzad Ebrahimi, Matthieu Chamik, Stefan Winkler, http://stefan.winkler.net/Publications/adip2004.pdf